Systems, Devices, and Methods for Cardiac Diagnosis and/or Monitoring

ABSTRACT

Some embodiments of the current disclosure are directed toward cardiac diagnosis and/or arrhythmia monitoring, and more particularly, systems, devices and methods for arrhythmia monitoring with a trained classifier including at least one neural network. In some embodiments, an external heart monitoring device may include a plurality ECG electrodes to sense surface ECG activity, ECG processing circuitry to process the surface ECG activity to provide at least one ECG signal, a non-transitory computer-readable medium comprising a rhythm change classifier comprising at least one neural network, and at least one processor to receive the ECG signal(s), detect with the rhythm change classifier time data corresponding to a predetermined rhythm change in the ECG signal(s), determine based on the detected time data at least one ECG signal portion corresponding to the predetermined rhythm change, and transmit the at least one determined ECG signal portion to a remote computer system.

CROSS REFERENCE TO RELATED APPLICATION

The present application is a continuation of U.S. patent applicationSer. No. 17/083,472, filed Oct. 29, 2020, which claims the benefit ofU.S. Provisional Patent Application No. 62/927,428, filed Oct. 29, 2019,the disclosures of each of which is hereby incorporated by reference intheir entireties.

FIELD OF THE DISCLOSURE

Embodiments of the current disclosure are directed toward cardiacdiagnosis and/or monitoring, and more particularly, systems, devices andmethods for arrhythmia monitoring with a trained classifier including atleast one neural network.

BACKGROUND OF THE DISCLOSURE

There is a wide variety of electronic and mechanical devices formonitoring and/or treating patients' medical conditions. In someexamples, depending on the underlying medical condition being monitoredand/or treated, medical devices such as cardiac monitors ordefibrillators may be surgically implanted or externally connected tothe patient. In some cases, physicians may use medical devices alone orin combination with drug therapies to treat conditions such as cardiacarrhythmias.

Such patients can include heart failure patients, e.g., congestive heartfailure (CHF) patients. CHF is a condition in which the heart's functionas a pump is inadequate to meet the body's needs. Generally, manydisease processes can impair the pumping efficiency of the heart tocause congestive heart failure. The symptoms of congestive heart failurevary, but can include fatigue, diminished exercise capacity, shortnessof breath, and swelling (edema). The diagnosis of congestive heartfailure is based on knowledge of the individual's medical history, acareful physical examination, and selected laboratory tests.

Additionally or alternatively, patients can suffer from cardiacarrhythmias. One of the most deadly cardiac arrhythmias is ventricularfibrillation, which occurs when normal, regular electrical impulses arereplaced by irregular and rapid impulses, causing the heart muscle tostop normal contractions and to begin to quiver. Normal blood flowceases, and organ damage or death can result in minutes if normal heartcontractions are not restored. Because the victim has no perceptiblewarning of the impending fibrillation, death often occurs before thenecessary medical assistance can arrive. Other cardiac arrhythmias caninclude excessively slow heart rates known as bradycardia or excessivelyfast heart rates known as tachycardia. Cardiac arrest can occur when apatient in which various arrhythmias of the heart, such as ventricularfibrillation, ventricular tachycardia, pulseless electrical activity(PEA), and asystole (heart stops all electrical activity) result in theheart providing insufficient levels of blood flow to the brain and othervital organs for the support of life.

Cardiac arrest and other cardiac health ailments are a major cause ofdeath worldwide. Various resuscitation efforts aim to maintain thebody's circulatory and respiratory systems during cardiac arrest in anattempt to save the life of the patient. The sooner these resuscitationefforts begin, the better the patient's chances of survival. Implantablecardioverter/defibrillators (ICDs) or external defibrillators (such asmanual defibrillators or automated external defibrillators (AEDs)) havesignificantly improved the ability to treat these otherwiselife-threatening conditions. Such devices operate by applying correctiveelectrical pulses directly to the patient's heart. Ventricularfibrillation or ventricular tachycardia can be treated by an implantedor external defibrillator, e.g., by providing a therapeutic shock to theheart in an attempt to restore normal rhythm. To treat conditions suchas bradycardia, an implanted or external pacing device can providepacing stimuli to the patient's heart until intrinsic cardiac electricalactivity returns. External pacemakers, defibrillators and other medicalmonitors designed for ambulatory and/or long-term use have furtherimproved the ability to timely detect and treat life-threateningconditions. Example external cardiac monitoring and/or treatment devicesinclude cardiac monitors, the ZOLL LifeVest® wearable cardioverterdefibrillator available from ZOLL Medical Corporation, and the AED Plusalso available from ZOLL Medical Corporation.

Certain cardiac monitoring and/or treatment devices may communicatebiometric data (e.g., ECG signal samples and/or ECG signalportions/strips) associated with the patient to the cloud (e.g., to aremote server, a cardiac monitoring facility, and/or the like) forreview (e.g., by technicians, prescribers/treating physicians, and/orthe like). However, such communication may require large bandwidth(e.g., all measured biometric data constantly streaming in real timeand/or the like), may include a large amount of biometric data notassociated with a cardiac event (e.g., biometric data associated withnormal sinus rhythm (NSR) and/or the like), and may require humanreviewers to review the large amounts of data without guidance as towhich portions thereof are associated with cardiac events. Additionallyor alternatively, it may be difficult to accurately determine whether ahuman reviewer correctly identified and/or annotated cardiac events inthe biometric data. For example, technicians may be different levels ofexperience and expertise, and failing to identify a cardiac event and/orfalsely identifying a cardiac event may result in harm to the patient(e.g., failure to provide needed treatment and/or providing treatmentthat is unnecessary, respectively).

SUMMARY OF SOME OF THE EMBODIMENTS

Embodiments of the current disclosure include an arrhythmia monitoringsystem. In some embodiments, the arrhythmia monitoring system mayinclude an external heart monitoring device for a patient. The externalheart monitoring device may include a plurality of electrocardiogram(ECG) electrodes configured to sense surface ECG activity of thepatient, ECG processing circuitry configured to process the surface ECGactivity of the patient to provide at least one ECG signal for thepatient on at least one ECG channel, a non-transitory computer-readablemedium including a rhythm change classifier, and at least one processoroperatively connected to the at least one ECG channel and thenon-transitory computer-readable medium. In some embodiments, the rhythmchange classifier may include at least one neural network trained basedon a historical collection of a plurality of ECG signal portions withknown rhythm change information. In some embodiments, the at least oneprocessor may be configured to receive the at least one ECG signalreceived via the at least one ECG channel; detect with the rhythm changeclassifier time data corresponding to a predetermined rhythm change inthe at least one ECG signal, the time data comprising at least one of astart time, a time interval, or any combination thereof; determine basedon the detected time data at least one ECG signal portion associatedwith the detected time data corresponding to the predetermined rhythmchange in the at least one ECG signal; and transmit the at least onedetermined ECG signal portion to a remote computer system.

In some embodiments, the predetermined rhythm change may be associatedwith an arrhythmia.

In some embodiments, the non-transitory computer readable medium mayinclude at least one of a memory, a programmable circuit board, a fieldprogrammable gate array, an integrated circuit, or any combinationthereof. In some embodiments, the at least one neural network mayinclude at least one of a convolutional neural network, a recurrentneural network, an attention network, a fully connected neural network,or any combination thereof.

In some embodiments, the at least one neural network may include atleast one convolutional neural network having a plurality ofconvolutional layers. Additionally or alternatively, the plurality ofconvolutional layers may include at least seven convolutional layers andno more than ten convolutional layers. Additionally or alternatively,the at least one convolutional network may further include an inputlayer and an output layer.

In some embodiments, the at least one ECG signal may include a pluralityof ECG signal samples. Additionally or alternatively, the input layermay include at least one node for each ECG signal sample of theplurality of ECG signal samples. In some embodiments, an output of theoutput layer may include an indication of the time data corresponding tothe rhythm change.

In some embodiments, the at least one ECG signal portion may include anECG signal portion having a duration greater than or equal to 15 secondsand less than or equal to 120 seconds. For example, the ECG signalportion may have a duration greater than or equal to 15 seconds and lessthan or equal to 60 seconds.

In some embodiments, the at least one ECG signal may include a pluralityof ECG signal samples. Additionally or alternatively, the plurality ofECG signal samples may be sampled at a rate greater than or equal to 100Hz and less than or equal to 500 Hz.

In some embodiments, the plurality of ECG signal samples may sampled ata rate greater than 100 Hz and less than 500 Hz.

In some embodiments, the at least one ECG channel may include aplurality of ECG channels. Additionally or alternatively, the at leastone ECG signal may include at least one respective ECG signal associatedwith each respective ECG channel of the plurality of ECG channels. Insome embodiments, the plurality of ECG channels may include a first ECGchannel and a second ECG channel. Additionally or alternatively, the atleast one ECG signal may include a first respective ECG signalassociated with the first ECG channel and a second respective ECG signalassociated with the second ECG channel. In some embodiments, the firstrespective ECG signal may be orthogonal to the second respective ECGsignal.

In some embodiments, the at least one neural network may include aplurality of Siamese branches. Additionally or alternatively, eachrespective Siamese branch of the plurality of Siamese branches may beassociated with a respective ECG channel of the plurality of ECGchannels. In some embodiments, the at least one neural network mayfurther include at least one further layer connected to the plurality ofSiamese branches. In some embodiments, each Siamese branch of theplurality of Siamese branches may include a plurality of convolutionallayers. Additionally or alternatively, dimensions of each of theplurality of convolutional layers of each respective Siamese branch maybe the same as the dimensions of each of the plurality of convolutionallayers of each other Siamese branch.

In some embodiments, the processor may be further configured to detectwith the rhythm change classifier the predetermined rhythm change basedon the at least one ECG signal.

In some embodiments, at least one sensor and associated sensor circuitrymay be configured to sense non-ECG biometric data of the patient.Additionally or alternatively, detecting the predetermined rhythm changemay be further based on the non-ECG biometric data of the patient. Insome embodiments, the at least one sensor may include at least one of anaccelerometer, a heart sound detector, or a combination thereof.Additionally or alternatively, the non-ECG biometric data may include atleast one of acceleration data, heart sound data, or any combinationthereof.

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one baseline ECG signal portion of thepatient. Additionally or alternatively, detecting the predeterminedrhythm change may be further based on at least one reference vector ofthe patient.

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one calibration measurement of the patient.Additionally or alternatively, the at least one calibration measurementmay be based on at least one second ECG signal from second surface ECGactivity sensed by a second plurality of ECG electrodes. Additionally oralternatively, the second plurality of ECG electrodes may be independentof the plurality of ECG electrodes of the external heart monitoringdevice.

In some embodiments, detecting the predetermined rhythm change may befurther based at least one previous ECG signal portion.

In some embodiments, a gateway device may be included. Additionally oralternatively, transmitting the at least one determined ECG signalportion to the remote computer system may include transmitting the atleast one determined ECG signal portion from the external heartmonitoring device to the gateway device. Additionally or alternatively,the gateway device may be configured to receive the at least onedetermined ECG signal portion from the external heart monitoring deviceand transmit the at least one determined ECG signal portion to theremote server.

In some embodiments, the remote computer system may be in communicationwith the external heart monitoring device. Additionally oralternatively, the remote computer system may be configured to receivethe at least one determined ECG signal portion from the external heartmonitoring device and/or analyze the at least one determined ECG signalportion to classify a type of arrhythmia for the rhythm change in the atleast one ECG signal. In some embodiments, the type of arrhythmia mayinclude at least one of a change in heart rate, atrial fibrillation,flutter, supraventricular tachycardia, ventricular tachycardia, pause,AV block, ventricular fibrillation, bigeminy, trigeminy, ventricularectopic beats, bradycardia, tachycardia, a change in morphology of theat least one ECG signal, or any combination thereof.

In some embodiments, the remote computer system may include anarrhythmia type classifier including at least one second neural networktrained based on a second historical collection of a second plurality ofECG signal portions with known arrhythmia type information. Additionallyor alternatively, analyzing the at least one determined ECG signalportion may include detecting with the arrhythmia type classifier thetype of arrhythmia associated with the rhythm change based on the atleast one determined ECG signal portion.

In some embodiments, the remote computer system may be furtherconfigured to transmit at least one message associated with the at leastone determined ECG signal portion and the type of arrhythmia associatedwith the rhythm change to a computing device associated with atechnician.

In some embodiments, the remote computer system may be furtherconfigured to analyze the at least one determined ECG signal portion toidentify a rare arrhythmia for the rhythm change in the at least one ECGsignal.

In some embodiments, the processor may be further configured todetermine with the rhythm change classifier a confidence scoreassociated with the predetermined rhythm change based on the at leastone ECG signal.

In some embodiments, the processor may be further configured to transmitat least one second ECG signal portion of the at least one ECG signal tothe remote computer system. Additionally or alternatively, the at leastone second ECG signal portion may be independent of the detected timedata corresponding to the predetermined rhythm change in the at leastone ECG signal. In some embodiments, the processor may be furtherconfigured to randomly determine the at least one second ECG signalportion. In some embodiments, the processor may be further configured todetermine with the rhythm change classifier a first confidence scoreassociated with the predetermined rhythm change based on the at leastone ECG signal, wherein the first confidence score is above a firstthreshold. Additionally or alternatively, the processor may be furtherconfigured to detect with the rhythm change classifier second time datacorresponding to a potential rhythm change in the at least one ECGsignal. Additionally or alternatively, the processor may be furtherconfigured to determine with the rhythm change classifier a secondconfidence score associated with the potential rhythm change based onthe at least one ECG signal, wherein the second confidence score isbelow the first threshold and above a second threshold. Additionally oralternatively, the processor may be further configured to determinebased on the detected second time data the at least one second ECGsignal portion associated with the detected second time datacorresponding to the potential rhythm change in the at least one ECGsignal.

In some embodiments, the remote computer system is further configured totransmit at least one message associated with the at least one secondECG signal portion to a computing device associated with a technicianand/or receive from the computing device associated with the technicianannotation data associated with at least one annotation for the at leastone second ECG signal portion.

In some embodiments, the remote computer system may be furtherconfigured to transmit the at least one annotation for the at least onesecond ECG signal portion to the external heart monitoring device.Additionally or alternatively, the processor may be further configuredto retrain the rhythm change classifier based on the at least one secondECG signal portion and the annotation data.

In some embodiments, the remote computer system may be furtherconfigured to add the at least one second ECG signal portion to thehistorical collection of the plurality of ECG signal portions.Additionally or alternatively, the known rhythm change information forthe at least one second ECG signal portion may include at least aportion of the annotation data. Additionally or alternatively, theremote computer system may be further configured to train an updatedrhythm change classifier based on the historical collection of theplurality of ECG signal portions with the known rhythm changeinformation. the remote computer system may be further configured totransmit the updated rhythm change classifier to the external heartmonitoring device. Additionally or alternatively, the processor may befurther configured to replace the rhythm change classifier with theupdated rhythm change classifier.

In some embodiments, the remote computer system may include anarrhythmia type classifier comprising at least one second neural networktrained based on a second historical collection of a second plurality ofECG signal portions with known arrhythmia type information. Additionallyor alternatively, the remote computer system may be further configuredto add the at least one second ECG signal portion to the secondhistorical collection of the second plurality of ECG signal portions.Additionally or alternatively, the known arrhythmia type information forthe at least one second ECG signal portion comprises at least a portionof the annotation data. Additionally or alternatively, the remotecomputer system may be further configured to retrain the arrhythmia typeclassifier based on the second historical collection of the secondplurality of ECG signal portions with known arrhythmia type information.

In some embodiments, the at least one determined ECG signal portion mayinclude a plurality of determined ECG signal portions. Additionally oralternatively, the remote computer system may be in communication withthe external heart monitoring device. Additionally or alternatively, theremote computer system may be configured to receive the plurality ofdetermined ECG signal portions from the external heart monitoring deviceand/or analyze each respective determined ECG signal portion of theplurality of determined ECG signal portions to classify a respectiveclass for each respective determined ECG signal potion. Additionally oralternatively, the class for at least two respective determined ECGsignal potions may include a first class. In some embodiments, theremote computer system may be further configured to transmit at leastone message associated with the at least two respective determined ECGsignal portions and the first class to a computing device associatedwith a technician.

In some embodiments, the processor may be further configured to detectwith the rhythm change classifier at least one of a count of peaks or aheart rate based on the at least one ECG signal. Additionally oralternatively, the processor may be further configured to determine thedetected at least one of the count of peaks or the heart rate is above afirst threshold for the patient or below a second threshold for thepatient. Additionally or alternatively, the second threshold for thepatient may be less than the first threshold for the patient.Additionally or alternatively, the processor may be further configuredto detect the predetermined rhythm change based on the at least one ofthe count of peaks or the heart rate being above the first threshold forthe patient or below the second threshold for the patient.

In some embodiments, the historical collection of the plurality of ECGsignal portions may include a second plurality of ECG signal portions ofat least one second ECG signal based on second surface ECG activitysensed by a second plurality of ECG electrodes. Additionally oralternatively, the second plurality of ECG electrodes independent of theplurality of ECG electrodes of the external heart monitoring device.

In some embodiments, the historical collection of the plurality of ECGsignal portions may include a first ECG signal portion associated with afirst time and a second ECG signal portion associated with a second timeafter the first time. Additionally or alternatively, the rhythm changeclassifier may be trained by predicting with the rhythm changeclassifier a predicted ECG signal portion associated with the secondtime based on the first ECG signal portion, determining at least oneerror value based on the predicted ECG signal portion and the second ECGsignal portion, and/or training the rhythm change classifier based onthe at least one error value. In some embodiments, the at least oneerror value may include one of a prediction error or a contrastive loss.

In some embodiments, the historical collection of the plurality of ECGsignal portions may include a first ECG signal portion associated with afirst time and a second ECG signal portion associated with a secondtime. Additionally or alternatively, the rhythm change classifier may betrained by predicting with the rhythm change classifier a predicted timeassociated with the second ECG signal portion based on the a first ECGsignal portion and the second ECG signal, determining at least one errorvalue based on the predicted time and the second time, and/or trainingthe rhythm change classifier based on the at least one error value.

Embodiments of the current disclosure include an arrhythmia detectionsystem. In some embodiments, the arrhythmia detection system may includea non-transitory computer-readable medium including an arrhythmia typeclassifier and at least one processor operatively connected to thenon-transitory computer readable medium. In some embodiments, thearrhythmia type classifier may include at least one neural networktrained based on a historical collection of a plurality of ECG signalportions with known arrhythmia type information. Additionally oralternatively, the at least one processor may be configured to receiveat least one ECG signal and annotation data associated with at least oneannotation for each of the at least one ECG signal, detect with thearrhythmia type classifier a type of arrhythmia in at least one ECGsignal and time data associated with the detected type of arrhythmia,determine based on the time data at least one ECG signal portionassociated with the detected type of arrhythmia in the at least one ECGsignal, determine a plausibility score for the at least one annotationbased on the detected type of arrhythmia, generate at least one messagebased on the at least one determined ECG signal portion and theplausibility score for the at least one annotation, and/or transmit theat least one message associated with the at least one determined ECGsignal portion. In some embodiments, the at least one message mayindicate at least one of a recommendation to annotate the at least onedetermined ECG signal portion based on the detected type of arrhythmiaor a recommendation to reevaluate the annotation data associated withthe at least one determined ECG signal portion based on the plausibilityscore.

In some embodiments, the at least one processor may be furtherconfigured to determine the plausibility score is below a threshold.Additionally or alternatively, generating the at least one message mayinclude generating, based on the determination that the plausibilityscore is below the threshold, the at least one message indicating therecommendation to reevaluate the annotation data associated with the atleast one determined ECG signal portion.

In some embodiments, the annotation data may be received from a firstcomputing device associated with a technician. Additionally oralternatively, the at least one message may be transmitted to a secondcomputing device associated with a supervisor of the technician.

In some embodiments, the at least one neural network may include atleast one deep neural network, a convolutional neural network, arecurrent neural network, an attention network, a fully connected neuralnetwork, or any combination thereof.

In some embodiments, the known arrhythmia type information may include aplurality of annotations. Additionally or alternatively, each annotationof the plurality of annotations may be associated with a respective ECGsignal portion of the plurality of ECG signal portions, Additionally oralternatively, the arrhythmia type classifier may be trained based onthe plurality of ECG signals and the plurality of annotations.

In some embodiments, the plurality of annotations may be from aplurality of technicians. Additionally or alternatively, each annotationof the plurality of annotations may be associated with a respectivetechnician of the plurality of technicians and the respective ECG signalportion of the plurality of ECG signal portions. Additionally oralternatively, the arrhythmia type classifier for a first technician ofthe plurality of technicians may be trained based on a subset of theplurality of ECG signals and the plurality of annotations associatedwith at least one other technician of the plurality of techniciansdifferent than the first technician. In some embodiments, eachannotation may be associated with at least one type of arrhythmiaassociated with the respective ECG signal portion.

In some embodiments, the historical collection of the plurality of ECGsignal portions may include a first plurality of ECG signal portionsassociated with at least one first ECG electrode and a second pluralityof ECG signal portions associated with at least one second ECGelectrode. Additionally or alternatively, each respective ECG signalportion of the second plurality of ECG signal portions may correspond toa respective ECG signal portion of the first plurality of ECG signalportions. Additionally or alternatively, the known arrhythmia typeinformation may include a plurality of annotations, and/or eachrespective annotation of the plurality of annotations may be associatedwith a respective ECG signal portion of the first plurality of ECGsignal portions. Additionally or alternatively, the arrhythmia typeclassifier may be trained by predicting with the arrhythmia typeclassifier a predicted type of arrhythmia in each respective ECG signalportion of the second plurality of ECG signal portions, determining atleast one error value based on the predicted type of arrhythmia and therespective annotation of the plurality of annotations associated with arespective ECG signal portion of the first plurality of ECG signalportions corresponding to the respective ECG signal portion of thesecond plurality of ECG signal portions, and training the arrhythmiatype classifier based on the at least one error value.

In some embodiments, the historical collection of the plurality of ECGsignal portions may include a first plurality of ECG signal portions ofat least one first ECG signal based on first surface ECG activity sensedby at least one first ECG electrode and a second plurality of ECG signalportions of at least one second ECG signal based on second surface ECGactivity sensed by at least one second ECG electrode. Additionally oralternatively, the at least one second ECG electrode may be independentof the at least one first ECG electrode. Additionally or alternatively,each ECG signal portion of the first plurality of ECG signal portionsmay be combined with a respective ECG signal portion of the secondplurality of ECG signal portions to form a plurality of extrapolated ECGsignal portion. Additionally or alternatively, the known arrhythmia typeinformation may include a plurality of annotations, and/or eachrespective annotation of the plurality of annotations may be associatedwith a respective extrapolated ECG signal portion of the plurality ofextrapolated ECG signal portions.

In some embodiments, at least some of the plurality of ECG signalportions of the historical collection may be time warped to form aplurality of warped ECG signal portions.

In some embodiments, at least some of the plurality of ECG signalportions of the historical collection may be at least one of filtered,inverted, or a combination thereof.

In some embodiments, at least one noise signal portion may be combinedwith at least some of the plurality of ECG signal portions of thehistorical collection.

In some embodiments, at least some of the plurality of ECG signalportions of the historical collection may be style transferred.

Embodiments of the current disclosure include an arrhythmia monitoringsystem. In some embodiments, the arrhythmia monitoring system mayinclude an external heart monitoring device for a patient and a gatewaydevice. The external heart monitoring device may include a plurality ofelectrocardiogram (ECG) electrodes configured to sense surface ECGactivity of the patient, ECG processing circuitry configured to processthe surface ECG activity of the patient to provide at least one ECGsignal for the patient on at least one ECG channel, and at least onefirst processor operatively connected to the at least one ECG channel.The first processor(s) may be configured to receive the ECG signal(s)received via the ECG channel(s) and transmit the ECG signal(s) (e.g., tothe gateway device). The gateway device may include a non-transitorycomputer-readable medium comprising a rhythm change classifier and atleast one second processor operatively connected to the non-transitorycomputer-readable medium. The rhythm change classifier may include atleast one neural network trained based on a historical collection of aplurality of ECG signal portions with known rhythm change information.The second processor(s) may be configured to receive the ECG signal(s)from the external heart monitoring device; detect with the rhythm changeclassifier time data corresponding to a predetermined rhythm change inthe ECG signal(s), the time data comprising at least one of a starttime, a time interval, or any combination thereof; determine based onthe detected time data at least one ECG signal portion associated withthe detected time data corresponding to the predetermined rhythm changein the ECG signal(s), and transmit the determined ECG signal portion(s)to a remote computer system.

Some embodiments of the current disclosure may include an arrhythmiamonitoring system, device, or method according to any one and/or anotherof the embodiments illustrated, described, and/or disclosed herein.

It should be appreciated that all combinations of the foregoing conceptsand additional concepts discussed in greater detail below (provided suchconcepts are not mutually inconsistent) are contemplated as being partof the inventive subject matter disclosed herein. In particular, allcombinations of claimed subject matter appearing at the end of thisdisclosure are contemplated as being part of the inventive subjectmatter disclosed herein. It should also be appreciated that terminologyexplicitly employed herein that also may appear in any disclosureincorporated by reference should be accorded a meaning most consistentwith the particular concepts disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The skilled artisan will understand that the drawings primarily are forillustrative purposes and are not intended to limit the scope of theinventive subject matter described herein. The drawings are notnecessarily to scale; in some instances, various aspects of theinventive subject matter disclosed herein may be shown exaggerated orenlarged in the drawings to facilitate an understanding of differentfeatures. In the drawings, like reference characters generally refer tolike features (e.g., functionally similar and/or structurally similarelements).

FIGS. 1A-C show example block diagrams of an environment for cardiacdiagnosis and/or arrhythmia monitoring, according to some embodiments.

FIGS. 2A-C shows example block diagrams of system architectures forcardiac diagnosis and/or arrhythmia monitoring, according to someembodiments.

FIGS. 3A-C show example swim lane diagrams of communication flows forexemplary processes for cardiac diagnosis and/or arrhythmia monitoring,according to some embodiments.

FIGS. 4A-C show example flowcharts of processes for cardiac diagnosisand/or arrhythmia monitoring, according to some embodiments.

FIG. 5A shows an example diagram of a neural network of an exemplaryrhythm change classifier, according to some embodiments.

FIG. 5B shows an example diagram of a neural network of an arrhythmiatype classifier, according to some embodiments.

FIGS. 6A-E show example ECG signal portions, according to someembodiments.

FIG. 7 shows an example block diagram of components of one or morecomputing devices on which the processes described herein can beimplemented, according to some embodiments.

FIG. 8 shows an example schematic illustration of measurement andtransmission of physiological data acquired via body-worn sensor(s)(e.g., external heart monitoring device(s)) disclosed herein, accordingto some embodiments.

FIGS. 9A-E show an example sensor(s) (e.g., of external heart monitoringdevice(s)) disclosed herein, a patch configured to hold the sensor(s) inproximity to a body, and attachment of a patch housing a sensor(s) ontoskin of a patient, according to some embodiments.

FIGS. 10A-C show example front, back and exploded views, respectively,of the sensor(s) (e.g., of external heart monitoring device(s))disclosed herein, according to some embodiments.

FIG. 11A shows an example illustration of device electronicsarchitecture for measurements and transmission of patient physiologicaldata (e.g., biometric data), according to some embodiments.

FIG. 11B shows a block diagram of example architecture of a radiofrequency (RF) module, according to some embodiments.

FIG. 11C shows a block diagram of another example architecture of an RFmodule, according to some embodiments.

FIG. 12 illustrates an example medical device (e.g., external heartmonitoring device) that is external, ambulatory, and wearable by apatient, according to some embodiments.

FIG. 13 illustrates an example component-level view of a medical device(e.g., external heart monitoring device), according to some embodiments.

DETAILED DESCRIPTION OF SOME OF THE EMBODIMENTS

This disclosure relates to systems, devices and methods for cardiacdiagnosis and/or arrhythmia monitoring, including heart failure statusmonitoring. For example, one or more trained classifier(s), each ofwhich including at least one neural network, may be used by a heartmonitoring device (e.g., external, wearable, and/or the like heartmonitoring device), computer system, and/or the like to detect (e.g.,identify and/or the like) a rhythm change, a type of arrhythmia, and/orthe like based on at least one ECG signal or portion(s) thereof. Exampleuse scenarios include use of the systems, devices, and methods forcardiac diagnosis and/or arrhythmia monitoring in the context of mobilecardiac telemetry, cardiac holter monitoring (including extended cardiacholter monitoring), wearable cardiac monitors, wearable defibrillators,wearable cardioverter defibrillators, and other such ambulatory cardiacmonitoring and/or treatment systems.

In some embodiments, an arrhythmia monitoring system may include anexternal heart monitoring device for a patient. For example, theexternal heart monitoring device may include a plurality ofelectrocardiogram (ECG) electrodes to sense surface ECG activity of thepatient. ECG processing circuitry may process the surface ECG activityof the patient to provide at least one ECG signal for the patient on atleast one ECG channel. A rhythm change classifier may be implemented ina non-transitory computer-readable medium (e.g., a memory, aprogrammable circuit board, a field programmable gate array, anintegrated circuit, any combination thereof, and/or the like). Therhythm change classifier may include at least one neural network trainedbased on a historical collection of a plurality of ECG signal portionswith known rhythm change information. Additionally, at least oneprocessor may be operatively connected to the ECG channel(s) and thenon-transitory computer-readable medium. The processor(s) may receivethe ECG signal(s) via the ECG channel(s). The processor(s) may also usethe rhythm change classifier to detect time data corresponding to arhythm change (e.g., predetermined rhythm change and/or the like) in theECG signal(s). For example, the time data may include a start time, atime interval, any combination thereof, and/or the like. Theprocessor(s) may also determine, based on the detected time data, atleast one ECG signal portion associated with the detected time datacorresponding to the predetermined rhythm change in the ECG signal(s).The processor(s) may also transmit the determined ECG signal portion(s)to a remote computer system (e.g., a remote server, a cardiac monitoringfacility, and/or the like).

For example, in such embodiments, the rhythm change classifier (e.g.,neural network(s) and/or the like thereof) may detect (e.g., identifyand/or the like) the rhythm change without having to classify thespecific rhythm type. For the purpose of illustration, the rhythm changeclassifier may detect a rhythm when heart rhythm changes from normalsinus rhythm (NSR) to Atrial Fibrillation (AFIB), from AFIB to NSR, fromAFIB to Atrial Flutter (AFL), from one morphology to another, and/or thelike. Additionally, every time a rhythm change is detected (e.g.,identified and/or the like), an ECG signal portions (e.g., ECG stripand/or the like) containing the detected rhythm change may betransmitted to the remote computer system (e.g., with an indication,message, marking, and/or the like indicating a detected rhythm change).Additionally or alternatively, the rhythm change classifier maydetermine a confidence score associated with the detected rhythm change(e.g., output the confidence score associated with the probability thatthe detected rhythm change actually is a rhythm change).

In some embodiments, the rhythm change classifier may also use (e.g.,receive as input for the neural network(s) and/or the like) dataassociated with additional sensors (e.g., non-ECG biometric data from atleast one sensor), including but not limited to accelerometer data,heart sound data, electromagnetic waves (e.g., radio frequency (RF)waves and/or the like) scattered and/or reflected from internal tissues,any combination thereof, and/or the like. Such additional non-ECGbiometric data may further enhance the accuracy, confidence, and/or thelike associated with the detection of rhythm changes.

In some embodiments, the rhythm change classifier may also use (e.g.,receive as input for the neural network(s), compare, and/or the like)data associated baseline ECG signal(s), reference vector(s), calibrationmeasurement(s), previous measurement(s), and/or the like for thepatient. For example, such baseline signal(s) and/or additionalmeasurement(s) may be acquired in a clinic using high-accuracyequipment, acquired by the external heart monitoring device during knownrest times, and/or the like. Use of such baseline signal(s) and/oradditional measurement(s) specific to the patient may further enhancethe accuracy, confidence, and/or the like associated with the detectionof rhythm changes.

In some embodiments, the use of a trained neural network to detect(e.g., identify and/or the like) the rhythm change(s) and only transmitthe ECG signal portion(s) when the rhythm change(s) is (are) detectedmay allow for reduced power consumption, e.g., suitable for a wearableexternal device with a relatively small power source (e.g., batteryand/or the like). Additionally, the rhythm change classifier (e.g.,neural network(s) thereof) may be implemented using low-power hardware(e.g., a programmable circuit board, a field programmable gate array, anintegrated circuit, and/or the like), which may further reduce the powerconsumption.

In some embodiments, classifier(s) and/or neural network logic thereofmay be distributed across multiple devices (e.g., the external heartmonitoring device, a gateway device, and/or the remote computer system),e.g., to optimize power consumption, to reduce the amount of datatransmitted, enhance accuracy of detection of rhythm changes, and/or toaddress other constraints. For example, the rhythm change classifier maybe implemented (e.g., completely, partially, and/or the like) on agateway device (e.g., mobile computing device, such as a smart phone,tablet, laptop computer, and/or the like), which may receive the ECGsignal data and/or other sensor data from the external heart monitoringdevice (e.g., via low-power wireless transmission, such as Bluetooth,Bluetooth Low Energy (BLE), and/or the like). Additionally oralternatively, a second classifier (e.g., larger and more accuraterhythm change classifier, arrhythmia type classifier, and/or the like)may be implemented (e.g., completely, partially, and/or the like) on theremote computer system.

In some embodiments, additional ECG signal portions may be sampled(e.g., randomly, at predetermined intervals, based on confidence scoresbelow an upper threshold but above a lower threshold, and/or the like)and transmitted from the external heart monitoring device to the remotecomputer system for annotation (e.g., by technicians and/or the like) inorder to test performance of the classifier(s) (e.g., rhythm changeclassifier, arrhythmia type classifier, and/or the like), to enlarge thetraining dataset (e.g., be added to the historical collection of ECGsignal portions and/or the like), to retrain the classifier(s), and/orthe like.

In some embodiments, ECG signal portions transmitted from the externalheart monitoring device to the remote computer system may be classified(e.g., bucketed and/or the like), grouped, and/or the like.Additionally, ECG signal portions in the same classification/group maybe presented to a user (e.g., technician and/or the like) together,which may enhance human review (e.g., processing, interrogation,annotation, and/or the like) of such ECG signal portion(s).

In some embodiments, an arrhythmia detection system may include at leastone non-transitory computer-readable medium (e.g., a memory, aprogrammable circuit board, a field programmable gate array, anintegrated circuit, any combination thereof, and/or the like) and atleast one processor may be operatively connected thereto. An arrhythmiatype classifier may be implemented in the non-transitorycomputer-readable medium. The arrhythmia type classifier may include atleast one neural network trained based on a historical collection of aplurality of ECG signal portions with known arrhythmia type information.The processor(s) may receive at least one ECG signal and annotation dataassociated with at least one annotation for each of the ECG signal(s).For example, the annotation data may be received from a computing deviceassociated with a technician. The processor(s) may detect, with thearrhythmia type classifier, a type of arrhythmia in the ECG signal(s)and time data associated with the detected type of arrhythmia. The timedata may include at least one of a start time, a time interval, or anycombination thereof. The processor(s) may determine, based on the timedata, at least one ECG signal portion associated with the detected typeof arrhythmia in the ECG signal(s). The processor(s) may determine aplausibility score for the annotation(s) based on the detected type ofarrhythmia. The processor(s) may generate at least one message based onthe determined ECG signal portion(s) and the plausibility score for theannotation(s). for example, the message(s) may indicate at least one ofa recommendation to annotate the determined ECG signal portion(s) basedon the detected type of arrhythmia, a recommendation to reevaluate theannotation data associated with the determined ECG signal portion(s)based on the plausibility score, and/or the like. The processor(s) maytransmit the message(s) associated with the ECG signal portion(s), e.g.,to the computing device of the technician.

For example, in such embodiments, technicians may have different levelsof experience, expertise, and/or the like. Such a system, using such anarrhythmia type classifier, may identify inconsistencies, anomalies,missed arrhythmias, and/or the like. Additionally or alternatively, sucha system may route (e.g., communicate, transmit, and/or the like) theidentified ECG signal portions to the technician, to another more seniortechnician (e.g., supervisor of the initial technician) and/or the like,for additional review (e.g., processing, interrogation, annotation,and/or the like).

Referring to FIGS. 1A-C, FIGS. 1A-C show example block diagrams of anenvironment 100 in which systems, products, and/or methods, as describedherein, may be implemented. As shown in FIGS. 1A-C, environment 100 mayinclude heart monitoring device 102, remote computer system 104, datarepository 106, technician device 108, supervisor device 110, and/orgateway device 129.

Heart monitoring device 102 may include one or more devices capable ofreceiving information from and/or communicating information to remotecomputer system 104, gateway device 129, data repository 106, techniciandevice 108, and/or supervisor device 110 (e.g., directly and/orindirectly via wired and/or wireless network and/or any other suitablecommunication technique). In some embodiments, heart monitoring device102 may be an external heart monitoring device (e.g., wearable by apatient, connected externally to a patient, and/or the like), asdescribed herein. In some embodiments, heart monitoring device 102 mayinclude a ECG electrodes 122 (e.g., a plurality of ECG electrodesconfigured to sense surface ECG activity of the patient), ECG processingcircuitry 124 (e.g., ECG processing circuitry configured to process thesurface ECG activity of the patient to provide at least one ECG signalfor the patient on at least one ECG channel), rhythm change classifier112 (e.g., implemented by at least one non-transitory computer readablemedium), processor 126 (e.g., at least one processor operativelyconnected to the at least one ECG channel and the non-transitorycomputer-readable medium), non-ECG sensors 128 (e.g., at least onesensor and associated sensor circuitry configured to sense non-ECGbiometric data of the patient), and/or the like, as described herein. Insome embodiments, rhythm change classifier 112 may include at least oneneural network trained based on historical collection of a plurality ofECG signal portions 116 with known rhythm change information 118, asdescribed herein. In some embodiments, processor 126 may be configuredto receive at least one ECG signal via the at least one ECG channel(e.g., from ECG processing circuitry 124), as described herein.Additionally or alternatively, processor 126 may be configured to detect(e.g., with rhythm change classifier 112) time data corresponding to apredetermined rhythm change in the ECG signal(s), as described herein.In some embodiments, the time data may include at least one of a starttime, a time interval, any combination thereof, and/or the like, asdescribed herein. Additionally or alternatively, processor 126 may beconfigured to determine (e.g., based on the detected time data) at leastone ECG signal portion associated with the detected time datacorresponding to the predetermined rhythm change in the at least one ECGsignal, as described herein. Additionally or alternatively, processor126 may be configured to transmit the at least one determined ECG signalportion to remote computer system 104, as described herein.

Remote computer system 104 may include one or more devices capable ofreceiving information from and/or communicating information to heartmonitoring device 102, gateway device 129, data repository 106,technician device 108, and/or supervisor device 110 (e.g., directlyand/or indirectly via wired and/or wireless network and/or any othersuitable communication technique). In some embodiments, remote computersystem 104 may include a server, a group of servers, and/or other likedevices, as described herein. Additionally or alternatively, remotecomputer system 104 may include at least one other computing deviceseparate from or including the server and/or group of servers, such as aportable and/or handheld device (e.g., a computer, a laptop, a personaldigital assistant (PDA), a smartphone, a tablet, and/or the like), adesktop computer, and/or other like devices, as described herein. Insome embodiments, remote computer system 104 may be associated with acardiac monitoring facility, and/or the like, as described herein.Additionally or alternatively, the cardiac monitoring facility may beassociated with a provider (e.g., manufacturer, distributor, and/or thelike) of heart monitoring device 102, as described herein. In someembodiments, remote computer system 104 may include at least oneclassifier (e.g., rhythm change classifier 112, arrhythmia typeclassifier 114, and/or the like), as described herein. Additionally oralternatively, each classifier (e.g., rhythm change classifier 112,arrhythmia type classifier 114, and/or the like) may be implemented byat least one non-transitory computer readable medium. In someembodiments, remote computer system 104 may include at least oneprocessor operatively connected to the non-transitory computer readablemedium, as described herein. In some embodiments, remote computer system104 may be in communication with data repository 106, which may be localor remote to remote computer system 104. In some embodiments, remotecomputer system 104 may be capable of receiving information from,storing information in, communicating information to, or searchinginformation stored in data repository 106.

In some embodiments, remote computer system 104 (e.g., processor(s)thereof) may be configured to receive at least one determined ECG signalportion from (external) heart monitoring device 102, analyze thedetermined ECG signal portion(s) to classify a type of arrhythmia forthe rhythm change in the ECG signal(s), and/or the like, as describedherein. For example, arrhythmia type classifier 114 may include at leastone (second) neural network trained based on a (second) historicalcollection of a (second) plurality of ECG signal portions 116 with knownarrhythmia type information 120, as described herein. In someembodiments, the type of arrhythmia may include at least one of a changein heart rate, atrial fibrillation, flutter, supraventriculartachycardia, ventricular tachycardia, pause, AV block, ventricularfibrillation, bigeminy, trigeminy, ventricular ectopic beats,bradycardia, tachycardia, a change in morphology of the at least one ECGsignal, any combination thereof, and/or the like, as described herein.

In some embodiments, remote computer system 104 (e.g., processor(s)thereof) may be configured to receive at least one ECG signal andannotation data associated with at least one annotation for each ECGsignal, as described herein. Additionally or alternatively, remotecomputer system 104 (e.g., processor(s) thereof) may be configured todetect (e.g., with arrhythmia type classifier 114) a type of arrhythmiain the ECG signal(s) and time data associated with the detected type ofarrhythmia, as described herein. In some embodiments, the time data mayinclude at least one of a start time, a time interval, any combinationthereof, and/or the like, as described herein. In some embodiments,remote computer system 104 (e.g., processor(s) thereof) may beconfigured to determine (e.g., based on the time data) at least one ECGsignal portion associated with the detected type of arrhythmia in theECG signal(s), as described herein. In some embodiments, remote computersystem 104 (e.g., processor(s) thereof) may be configured to determine aplausibility score for the annotation(s) based on the detected type ofarrhythmia, as described herein. In some embodiments, remote computersystem 104 (e.g., processor(s) thereof) may be configured to generate atleast one message based on the determined ECG signal portion(s) and theplausibility score for the annotation(s), as described herein.Additionally or alternatively, the message(s) may indicate at least oneof a recommendation to annotate the determined ECG signal portion(s)based on the detected type of arrhythmia, a recommendation to reevaluatethe annotation data associated with the determined ECG signal portion(s)based on the plausibility score, and/or the like, as described herein.In some embodiments, remote computer system 104 (e.g., processor(s)thereof) may be configured to transmit the message(s) associated withthe determined ECG signal portion(s) (e.g., to technician device 108,supervisor device 110, and/or the like).

Data repository 106 may include one or more devices capable of receivinginformation from and/or communicating information to heart monitoringdevice 102, remote computer system 104, gateway device 129, techniciandevice 108, and/or supervisor device 110 (e.g., directly and/orindirectly via wired and/or wireless network and/or any other suitablecommunication technique). In some embodiments, data repository 106 mayinclude a server, a group of servers, and/or other like devices, asdescribed herein. In some embodiments, data repository 106 may beassociated with a cardiac monitoring facility, and/or the like, asdescribed herein. Additionally or alternatively, the cardiac monitoringfacility may be associated with a provider (e.g., manufacturer,distributor, and/or the like) of heart monitoring device 102, asdescribed herein. In some embodiments, data repository 106 may be partof remote computer system 104. Additionally or alternatively, datarepository 106 may be local or remote to remote computer system 104. Insome embodiments, remote computer system 104 may be capable of receivinginformation from, storing information in, communicating information to,or searching information stored in data repository 106.

In some embodiments, data repository 106 may include at least onehistorical collection of a plurality of ECG signal portions 116, asdescribed herein. Additionally or alternatively, data repository 106 mayinclude known rhythm change information 118 for at least some of thehistorical collection of a plurality of ECG signal portions 116 (e.g., afirst plurality of ECG signal portions and/or the like), as describedherein. Additionally or alternatively, data repository 106 may includeknown arrhythmia type information 120 for at least some of thehistorical collection of a plurality of ECG signal portions 116 (e.g., asecond plurality of ECG signal portions and/or the like), as describedherein. In some embodiments, the data repository 106 may includepatient-specific information, e.g., baseline ECG and/or other baselinephysiological information, reference ECG and/or other referencephysiological information specific to the patient, patient-specificcalibration data, and/or the like. For example, prior to initialdeployment on the patient, heart monitoring device 102 may be fitted onthe patient, and/or initial patient-specific information (e.g., baselineECG and/or physiological data and/or the like) may be determined usingheart monitoring device 102. Such patient-specific information may bedesignated as the baseline data for the patient. Additionally oralternatively, certain patient-specific data may be designated asreference data for certain analysis, as described herein. In someembodiments, data repository 106 may include patient-specificclassifiers (e.g., patient-specific rhythm change classifiers,patient-specific arrhythmia type classifiers, and/or the like) that maybe trained as described herein based on such baseline and/or referencedata.

Technician device 108 may include one or more devices associated with atechnician and capable of receiving information from and/orcommunicating information to heart monitoring device 102, remotecomputer system 104, gateway device 129, data repository 106, and/orsupervisor device 110 (e.g., directly and/or indirectly via wired and/orwireless network and/or any other suitable communication technique). Insome embodiments, technician device 108 may include at least onecomputing device, such as a portable and/or handheld device (e.g., acomputer, a laptop, a personal digital assistant (PDA), a smartphone, atablet, and/or the like), a desktop computer, and/or other like devices,as described herein. In some embodiments, technician device 108 may beassociated with a cardiac monitoring facility, and/or the like, asdescribed herein. Additionally or alternatively, the cardiac monitoringfacility may be associated with a provider (e.g., manufacturer,distributor, and/or the like) of heart monitoring device 102, asdescribed herein. In some embodiments, technician device 108 may be partof remote computer system 104. Additionally or alternatively, techniciandevice 108 may be local or remote to remote computer system 104. In someembodiments, technician device 108 may include at least one inputcomponent that permits technician device 108 to receive information,such as via user input (e.g., a touch screen display, a keyboard, akeypad, a mouse, a button, a switch, a microphone, a camera, and/or thelike). Additionally or alternatively, technician device 108 may includeat least one output component that provides output information fromtechnician device 108 (e.g., a display, a touch screen, a speaker,and/or the like). In some embodiments, technician device 108 may receivemessages (e.g., recommendations regarding annotations, identificationsof types of arrhythmia, identifications of rhythm changes,classifications, and/or the like) associated with ECG signals and/orportions thereof (e.g., from remote computer system 104, heartmonitoring device 102, supervisor device 110, and/or the like), asdescribed herein. Additionally or alternatively, technician device 108may communicate annotation data associated with at least one annotationfor a respective ECG signal and/or portion thereof (e.g., to remotecomputer system 104, heart monitoring device 102, supervisor device 110,and/or the like), as described herein.

Supervisor device 110 may include one or more devices associated with asupervisor (e.g., supervisor of at least one technician and/or the like)and capable of receiving information from and/or communicatinginformation to heart monitoring device 102, remote computer system 104,gateway device 129, data repository 106, and/or technician device 108(e.g., directly and/or indirectly via wired and/or wireless networkand/or any other suitable communication technique). In some embodiments,supervisor device 110 may include at least one computing device, such asa portable and/or handheld device (e.g., a computer, a laptop, apersonal digital assistant (PDA), a smartphone, a tablet, and/or thelike), a desktop computer, and/or other like devices, as describedherein. In some embodiments, supervisor device 110 may be associatedwith a cardiac monitoring facility, and/or the like, as describedherein. Additionally or alternatively, the cardiac monitoring facilitymay be associated with a provider (e.g., manufacturer, distributor,and/or the like) of heart monitoring device 102, as described herein. Insome embodiments, supervisor device 110 may be part of remote computersystem 104. Additionally or alternatively, supervisor device 110 may belocal or remote to remote computer system 104. In some embodiments,supervisor device 110 may include at least one input component thatpermits supervisor device 110 to receive information, such as via userinput (e.g., a touch screen display, a keyboard, a keypad, a mouse, abutton, a switch, a microphone, a camera, and/or the like). Additionallyor alternatively, supervisor device 110 may include at least one outputcomponent that provides output information from supervisor device 110(e.g., a display, a touch screen, a speaker, and/or the like). In someembodiments, supervisor device 110 may receive messages (e.g.,recommendations regarding annotations, identifications of types ofarrhythmia, identifications of rhythm changes, classifications, and/orthe like) associated with ECG signals and/or portions thereof (e.g.,from remote computer system 104, heart monitoring device 102, techniciandevice 108, and/or the like), as described herein. Additionally oralternatively, supervisor device 110 may communicate annotation dataassociated with at least one annotation for a respective ECG signaland/or portion thereof (e.g., to remote computer system 104, heartmonitoring device 102, technician device 108, and/or the like), asdescribed herein.

Gateway device 129 may include one or more devices capable of receivinginformation from and/or communicating information to heart monitoringdevice 102, remote computer system 104, data repository 106, techniciandevice 108, and/or supervisor device 110 (e.g., directly and/orindirectly via wired and/or wireless network and/or any other suitablecommunication technique). In some embodiments, gateway device 129 mayinclude at least one computing device, such as a portable and/orhandheld device (e.g., a computer, a laptop, a personal digitalassistant (PDA), a smartphone, a tablet, and/or the like), a desktopcomputer, and/or other like devices, as described herein. In someembodiments, gateway device 129 may be associated with a patient, e.g.,a respective patient associated with (e.g., connected to, implantedwith, and/or the like) heart monitoring device 102. In some embodiments,gateway device 129 may be associated with a cardiac monitoring facility,and/or the like, as described herein. Additionally or alternatively, thecardiac monitoring facility may be associated with a provider (e.g.,manufacturer, distributor, and/or the like) of heart monitoring device102, as described herein. In some embodiments, gateway device 129 may belocal or remote to remote heart monitoring system 102. Additionally oralternatively, gateway device 129 may be local or remote to remotecomputer system 104. In some embodiments, gateway device 129 may includeat least one input component that permits gateway device 129 to receiveinformation, such as via user input (e.g., a touch screen display, akeyboard, a keypad, a mouse, a button, a switch, a microphone, a camera,and/or the like). Additionally or alternatively, gateway device 129 mayinclude at least one output component that provides output informationfrom gateway device 129 (e.g., a display, a touch screen, a speaker,and/or the like). In some embodiments, gateway device 129 may receivebiometric data (e.g., ECG signals, ECG signal portions, non-ECGbiometric data associated with at least one sensor, and/or the like)from heart monitoring device 102 and/or the like, as described herein.Additionally or alternatively, gateway device 129 may communicatebiometric data (e.g., ECG signals, ECG signal portions, non-ECGbiometric data associated with at least one sensor, and/or the like) toremote computer system 104 and/or the like, as described herein. In someembodiments, rhythm change classifier 112 may be implemented (e.g.,completely, partially, and/or the like) by a non-transitory computerreadable medium of gateway device 129 (e.g., independent of, in lieu of,or in addition to heart monitoring device 102). Additionally oralternatively, a processor of gateway device 129 may be configured toreceive at least one ECG signal (e.g., from heart monitoring device 102)and/or detect (e.g., with rhythm change classifier 112) time datacorresponding to a predetermined rhythm change in the ECG signal(s), asdescribed herein. In some embodiments, the time data may include atleast one of a start time, a time interval, any combination thereof,and/or the like, as described herein. Additionally or alternatively, aprocessor of gateway device 129 may be configured to determine (e.g.,based on the detected time data) at least one ECG signal portionassociated with the detected time data corresponding to thepredetermined rhythm change in the at least one ECG signal and/ortransmit the at least one determined ECG signal portion to remotecomputer system 104, as described herein.

In some embodiments, heart monitoring device 102, remote computer system104, data repository 106, technician device 108, supervisor device 110,and/or gateway device 129 may be connected by one or more networks. Thenetwork(s) may include one or more wired and/or wireless networks. Forexample, the network(s) may include a cellular network (e.g., along-term evolution (LTE) network, a third generation (3G) network, afourth generation (4G) network, a code division multiple access (CDMA)network, and/or the like), a public land mobile network (PLMN), a localarea network (LAN), a wide area network (WAN), a metropolitan areanetwork (MAN), a telephone network (e.g., the public switched telephonenetwork (PSTN)), a private network, a virtual private network (VPN), alocal network, an ad hoc network, an intranet, the Internet, a fiberoptic-based network, a cloud computing network, and/or the like, and/ora combination of these or other types of networks.

Referring now to FIG. 2A, FIG. 2A shows an example block diagram of asystem architecture 200 a for arrhythmia monitoring, according to someembodiments. In addition to system components, the system architecture200 a also shows data flows between the system components. As shown inFIG. 2A, system architecture 200 a may include heart monitoring device202 a, remote computer system 204 a, data repository 206 a, techniciandevice 208 a, and/or gateway device 229 a. In some embodiments, heartmonitoring device 202 a may be the same as or similar to heartmonitoring device 102. In some embodiments, remote computer system 204 amay be the same as or similar to remote computer system 104 (e.g., oneor more devices of remote computer system 104). In some embodiments,data repository 206 a may be the same as or similar to data repository106 (e.g., one or more devices of data repository 106). In someembodiments, technician device 208 a may be the same as or similar totechnician device 108. In some embodiments, gateway device 229 a may bethe same as or similar to gateway device 129.

As shown in FIG. 2A, at 230 a, remote computer system 204 a may receive(e.g., retrieve, search for, send a request and/or query to cause datarepository 206 a to communicate, and/or the like) a historicalcollection of a plurality of ECG signal portions and information relatedthereto (e.g., known rhythm change information, known arrhythmia typeinformation, and/or the like), e.g., from data repository 206 a, asdescribed herein. In some embodiments, remote computer system 204 a maytrain at least one neural network of at least one classifier (e.g., arhythm change classifier, an arrhythmia type classifier, and/or thelike) based on the historical collection of a plurality of ECG signalportions and information related thereto (e.g., known rhythm changeinformation, known arrhythmia type information, and/or the like,respectively), as described herein.

In some embodiments, a rhythm change classifier may include at least oneneural network, as described herein. Additionally or alternatively, theat least one neural network may include at least one of a convolutionalneural network, a recurrent neural network, an attention network, afully connected neural network, any combination thereof, and/or thelike. For example, the neural network(s) may include at least oneconvolutional neural network having a plurality of convolutional layers.In some embodiments, the convolutional neural network(s) may includebetween five and 40 convolutional layers (e.g., at least fiveconvolutional layers and up to 40 convolutional layers). For example,the convolutional neural network(s) may include between seven and tenconvolutional layers (e.g., at least seven convolutional layers and nomore than ten convolutional layers). In some embodiments, eachconvolutional layer may include at least one convolutional nodes (e.g.,a plurality of convolutional nodes). In some embodiments, theconvolutional neural network(s) may further include an input layer andan output layer. For example, the ECG signal(s) may include a pluralityof ECG signal samples. Additionally or alternatively, the input layermay include at least one node for each ECG signal sample of theplurality of ECG signal samples (or a subset thereof, e.g., associatedwith a predetermined time period, a buffer size of a buffer for ECGsignal samples, and/or the like). Additionally or alternatively, theinput layer may further include at least one input for non-ECG biometricdata associated with each sensor of heart monitoring device 202 a, asdescribed herein. In some embodiments, an output of the output layer mayinclude an indication of the time data corresponding to the rhythmchange. Additionally or alternatively, an output of the output layer mayinclude a confidence score, as described herein. In some embodiments,the neural network(s) may include a plurality of Siamese branches (e.g.,each respective Siamese branch associated with a respective ECGchannel), as described herein.

In some embodiments, remote computer system 204 a may train the rhythmchange classifier. For example, remote computer system 204 a may trainthe rhythm change classifier by generating, with the rhythm changeclassifier, predicted rhythm change information (e.g., data (e.g.,probability, confidence score, and/or the like) associated with apredicted rhythm change, data (e.g., probability, confidence score,and/or the like) associated with a lack of a predicted rhythm change,and/or the like) for each ECG signal portion of the historicalcollection of the plurality of ECG signal portions (or a first pluralitythereof), determining at least one error value based on the predictedrhythm change information and the known rhythm change information, andupdating the rhythm change classifier (e.g., updating the weightsthereof and/or the like) based on the error value(s) (e.g., using backpropagation and/or the like). In some embodiments, the error value(s)may include one of a prediction error or a contrastive loss.

In some embodiments, the historical collection of the plurality of ECGsignal portions may include a first ECG signal portion associated with afirst time and a second ECG signal portion associated with a second timeafter the first time. Additionally or alternatively, remote computersystem 204 a may train the rhythm change classifier by generating, withthe rhythm change classifier, a predicted ECG signal portion associatedwith the second time based on the first ECG signal portion, determiningat least one error value based on the predicted ECG signal portion andthe second ECG signal portion, and updating the rhythm change classifier(e.g., updating the weights thereof and/or the like) based on the errorvalue(s) (e.g., using back propagation and/or the like). In someembodiments, the error value(s) may include one of a prediction error ora contrastive loss.

In some embodiments, the historical collection of the plurality of ECGsignal portions may include a first ECG signal portion associated with afirst time and a second ECG signal portion associated with a secondtime. Additionally or alternatively, remote computer system 204 a maytrain the rhythm change classifier by generating, with the rhythm changeclassifier, a predicted time associated with the second ECG signalportion based on the a first ECG signal portion and the second ECGsignal, determining at least one error value based on the predicted timeand the second time, and updating the rhythm change classifier (e.g.,updating the weights thereof and/or the like) based on the errorvalue(s) (e.g., using back propagation and/or the like). In someembodiments, the error value(s) may include one of a prediction error ora contrastive loss.

In some embodiments, there may be an insufficient number of ECG signalportions in the historical collection of the plurality of ECG signalportions with known rhythm change information to train the rhythm changeclassifier to perform the desired task (e.g., detect and/or identify atleast one predetermined rhythm changes). Additionally or alternatively,there may be sufficient data (e.g., historical ECG signal portionsand/or the like) to train the rhythm change classifier to perform aseparate task (e.g., which may be related in some way to the targettask). In some embodiments, the rhythm change classifier may be trainedto perform the separate task (e.g., counting R-peaks, determining heartrate, and/or the like based on the ECG signal(s)). Additionally oralternatively, the rhythm change classifier may then be adapted toperform the target task. For example, in some embodiments, the rhythmchange classifier may be retrained using the limited amount of ECGsignal portions in the historical collection of the plurality of ECGsignal portions with known rhythm change information and/or the like.Additionally or alternatively, the rhythm change classifier may be usedto perform the separate task (e.g., counting R-peaks, determining heartrate, and/or the like based on the ECG signal(s)), and the outputthereof may be applied to the target task. For example, the processor(e.g., of heart monitoring device 202 a and/or gateway device 229 a) maydetect, with the rhythm change classifier, at least one of a count ofpeaks or a heart rate based on the at least one ECG signal. Additionallyor alternatively, the processor (e.g., of heart monitoring device 202 aand/or gateway device 229 a) may determine the detected at least one ofthe count of peaks or the heart rate is above a first threshold (e.g.,tachycardia onset threshold) for the patient or below a second threshold(e.g., bradycardia onset threshold) for the patient (e.g., wherein thesecond threshold for the patient may be less than the first thresholdfor the patient). Additionally or alternatively, the processor (e.g., ofheart monitoring device 202 a and/or gateway device 229 a) may detectthe predetermined rhythm change based on the at least one of the countof peaks or the heart rate being above the first threshold (e.g.,tachycardia onset threshold) for the patient or below the secondthreshold (e.g., bradycardia onset threshold) for the patient.

In some embodiments, there may be an insufficient number of ECG signalportions associated with (e.g., sensed from and/or the like) theplurality of ECG electrodes of the heart monitoring device 202 a in thehistorical collection of the plurality of ECG signal portions with knownrhythm change information to train the rhythm change classifier for theECG signal(s) received form the plurality of ECG electrodes of the heartmonitoring device 202 a. Additionally or alternatively, there may besufficient data (e.g., historical ECG signal portions and/or the like)associated with (e.g., sensed from and/or the like) a second pluralityof ECG electrodes (e.g., electrodes from an ECG device separate from theheart monitoring device 202 a, such as a 12-lead ECG sensor, a separateexternal and/or wearable heart monitoring device, and/or the like)independent of the plurality of ECG electrodes of the heart monitoringdevice 202 a to train the rhythm change classifier based on the secondplurality of ECG electrodes. In some embodiments, the rhythm changeclassifier may be trained based on the ECG signal portion(s) associatedwith (e.g., sensed from and/or the like) the second plurality of ECGelectrodes. Additionally or alternatively, the rhythm change classifiermay then be adapted to detect the predetermined rhythm change(s) basedon the plurality of ECG electrodes of the heart monitoring device 202 a.In some embodiments, remote computer system 204 a may determine (e.g.,calculate and/or the like) a transform (e.g., vector projection and/orthe like) of the ECG signal portion(s) associated with the secondplurality of ECG electrodes to the plurality of ECG electrodes of theheart monitoring device 202 a, and the transform of the ECG signalportion(s) may be used to train the rhythm change classifier as if theECG signal portions were associated with (e.g., sensed from and/or thelike) the plurality of ECG electrodes of the heart monitoring device 202a.

In some embodiments, remote computer system 204 a may train anarrhythmia type classifier by predicting, with the arrhythmia typeclassifier, a predicted type of arrhythmia in each respective ECG signalportion of the historical collection of the plurality of ECG signalportions (or a second plurality thereof), determining at least one errorvalue based on the predicted type of arrhythmia and the known arrhythmiatype information (e.g., a respective annotations associated with a knowntype of arrhythmia for each respective ECG signal portion), and trainingthe arrhythmia type classifier (e.g., updating the weights thereofand/or the like) based on the error value(s) (e.g., using backpropagation and/or the like). In some embodiments, the error value(s)may include one of a prediction error or a contrastive loss.

In some embodiments, an arrhythmia type classifier may include at leastone neural network (e.g., at least one second neural network), asdescribed herein. Additionally or alternatively, the at least one(second) neural network may include at least one of a deep neuralnetwork, a convolutional neural network, a recurrent neural network, anattention network, a fully connected neural network, any combinationthereof, and/or the like. For example, the neural network(s) may includeat least one convolutional neural network having a plurality ofconvolutional layers. In some embodiments, the convolutional neuralnetwork(s) may include between five and 40 convolutional layers (e.g.,at least five convolutional layers and up to 40 convolutional layers).For example, the convolutional neural network(s) may include betweenseven and ten convolutional layers (e.g., at least seven convolutionallayers and no more than ten convolutional layers). In some embodiments,each convolutional layer may include at least one convolutional nodes(e.g., a plurality of convolutional nodes). In some embodiments, theconvolutional neural network(s) may further include an input layer andan output layer. For example, the ECG signal(s) may include a pluralityof ECG signal samples. Additionally or alternatively, the input layermay include at least one node for each ECG signal sample of theplurality of ECG signal samples (or a subset thereof, e.g., associatedwith a predetermined time period, a buffer size of a buffer for ECGsignal samples, and/or the like). Additionally or alternatively, theinput layer may further include at least one input for non-ECG biometricdata associated with sensors (e.g., of heart monitoring device 202 aand/or the like), as described herein. In some embodiments, an output ofthe output layer may include an indication of the time datacorresponding to the arrhythmia type. Additionally or alternatively, anoutput of the output layer may include a confidence score, aplausibility score, and/or the like, as described herein. In someembodiments, the neural network(s) may include a plurality of Siamesebranches (e.g., each respective Siamese branch associated with arespective ECG channel), as described herein.

As shown in FIG. 2A, at 232 a, remote computer system 204 a maycommunicate a trained rhythm change classifier (or a plurality ofweights thereof) to heart monitoring device 202 a and/or gateway device229 a, as described herein. In some embodiments, after training, aplurality of weights corresponding to the trained rhythm changeclassifier may be communicated to heart monitoring device 202 a and/orgateway device 229 a. Additionally or alternatively, a copy of thetrained rhythm change classifier (or a plurality of weights thereof) maybe downloaded from remote computer system 204 a and/or installed on(e.g., uploaded to, written to, configured on, and/or the like) at leastone non-transitory computer readable medium (e.g., e.g., a memory, aprogrammable circuit board, a field programmable gate array (FPGA), anintegrated circuit, any combination thereof, and/or the like), which maybe installed in and/or part of heart monitoring device 202 a and/orgateway device 229 a.

In some embodiments, heart monitoring device 202 a may be an externalheart monitoring device for a patient, as described herein. For example,(external) heart monitoring device 202 a may include a plurality of ECGelectrodes configured to sense surface ECG activity of the patient.Additionally or alternatively, (external) heart monitoring device 202 amay include ECG processing circuitry configured to process the surfaceECG activity of the patient to provide at least one ECG signal for thepatient on at least one ECG channel.

In some embodiments, (external) heart monitoring device 202 a mayinclude a non-transitory computer-readable medium (e.g., a memory, aprogrammable circuit board, a field programmable gate array, anintegrated circuit, any combination thereof, and/or the like) including(e.g., implementing, embodying, storing, and/or the like) the trainedrhythm change classifier (which may include, e.g., at least one neuralnetwork trained based on the historical collection of a plurality of ECGsignal portions with known rhythm change information), as describedherein. Additionally or alternatively, (external) heart monitoringdevice 202 a may include at least one processor operatively connected tothe ECG channel(s) and/or the non-transitory computer-readable medium.

In some embodiments, gateway device 229 a may include a non-transitorycomputer-readable medium (e.g., a memory, a programmable circuit board,a field programmable gate array, an integrated circuit, any combinationthereof, and/or the like) including (e.g., implementing, embodying,storing, and/or the like) the trained rhythm change classifier (whichmay include, e.g., at least one neural network trained based on thehistorical collection of a plurality of ECG signal portions with knownrhythm change information), as described herein. Additionally oralternatively, gateway device 229 a may include at least one processoroperatively connected to the non-transitory computer-readable medium.

In some embodiments, heart monitoring device 202 a (e.g., theprocessor(s) thereof) may be configured to receive the ECG signal(s) viathe ECG channel(s). Additionally or alternatively, heart monitoringdevice 202 a (e.g., the processor(s) thereof) may be configured todetect with the rhythm change classifier time data corresponding to apredetermined rhythm change in the at least one ECG signal. For example,the predetermined rhythm change may be associated with an arrhythmia(e.g., a change in heart rate, atrial fibrillation, flutter,supraventricular tachycardia, ventricular tachycardia, pause, AV block,ventricular fibrillation, bigeminy, trigeminy, ventricular ectopicbeats, bradycardia, tachycardia, a change in morphology of the at leastone ECG signal, any combination thereof, and/or the like). Additionallyor alternatively, the time data may include at least one of a starttime, a time interval, any combination thereof, and/or the like. In someembodiments, heart monitoring device 202 a (e.g., the processor(s)thereof) may be configured to determine based on the detected time dataat least one ECG signal portion associated with the detected time datacorresponding to the predetermined rhythm change in the ECG signal(s).

In some embodiments, heart monitoring device 202 a (e.g., theprocessor(s) thereof) may be configured to receive the ECG signal(s) viathe ECG channel(s). Additionally or alternatively, heart monitoringdevice 202 a (e.g., the processor(s) thereof) may be configured totransmit the ECG signal(s) to gateway device 229 a. In some embodiments,gateway device 229 a (e.g., the processor(s) thereof) may be configuredto detect with the rhythm change classifier time data corresponding to apredetermined rhythm change in the ECG signal(s). For example, thepredetermined rhythm change may be associated with an arrhythmia (e.g.,a change in heart rate, atrial fibrillation, flutter, supraventriculartachycardia, ventricular tachycardia, pause, AV block, ventricularfibrillation, bigeminy, trigeminy, ventricular ectopic beats,bradycardia, tachycardia, a change in morphology of the at least one ECGsignal, any combination thereof, and/or the like), as described herein.Additionally or alternatively, the time data may include at least one ofa start time, a time interval, any combination thereof, and/or the like.In some embodiments, gateway device 229 a (e.g., the processor(s)thereof) may be configured to determine based on the detected time dataat least one ECG signal portion associated with the detected time datacorresponding to the predetermined rhythm change in the ECG signal(s).

In some embodiments, the ECG signal portion(s) may have a durationgreater than or equal to 15 seconds and less than or equal to 120seconds (e.g., based on a predetermined time interval, a buffer size ofa buffer for ECG signal samples, and/or the like). For example, the ECGsignal portion(s) may have a duration greater than or equal to 15seconds and less than or equal to 60 seconds. In some embodiments, theECG signal portion(s) may have a duration of between 15 seconds and 180seconds. In some embodiments, the ECG signal portion(s) may have aduration of between 15 seconds and 240 seconds. In some embodiments, theECG signal portion(s) may have a duration of between 15 seconds and 480seconds. In some embodiments, the ECG signal portion(s) may have aduration of between 15 seconds and 1000 seconds. In some embodiments,the duration of the ECG signal portion(s) may be specified via a userinput via a user interface control (e.g., of heart monitoring device 202a, gateway device 229 a, and/or the like).

In some embodiments, the ECG signal(s) comprises a plurality of ECGsignal samples. For example, the ECG signal sample(s) may be sampled ata rate between 10 Hz and 1000 Hz (e.g., greater than or equal to 10 Hzand less than or equal to 1000 Hz), a rate between 100 Hz and 500 Hz(e.g., greater than or equal to 100 Hz and less than or equal to 500Hz), and/or the like.

In some embodiments, the ECG channel(s) may include a plurality of ECGchannels (e.g., channels of a standard 12-lead ECG system, a subsetthereof, and/or the like). Additionally or alternatively, the ECGsignal(s) may include at least one respective ECG signal associated witheach respective ECG channel. In some embodiments, the plurality of ECGchannels may include a first ECG channel and a second ECG channel.Additionally or alternatively, the ECG signal(s) may include a firstrespective ECG signal associated with the first ECG channel and a secondrespective ECG signal associated with the second ECG channel. In someembodiments, the first respective ECG signal may be substantiallyorthogonal to the second respective ECG signal. For example, the firstECG channel may be associated with a first electrode positionedproximate a front of the patient and a second electrode positionedproximate a back of the patient (e.g., a front-to-back (FB) channel).Additionally or alternatively, the second ECG channel may be associatedwith a third electrode positioned proximate a first side of the patientand a fourth electrode positioned proximate a second side of the patient(e.g., a side-to-side (SS) channel). Additionally or alternatively, theFB channel may be orthogonal to the SS channel (e.g., a firsthypothetical line connecting the first electrode to the second electrodemay be substantially (e.g., approximately and/or the like) orthogonal(e.g., perpendicular and/or the like) to a second hypothetical lineconnecting the third electrode to the fourth electrode, the secondhypothetical line may have a component (e.g., vector component, vectorprojection, and/or the like that may be calculated and/or the like) thatis orthogonal to the first hypothetical line, and/or the like).

In some embodiments, the neural network(s) of the rhythm changeclassifier (e.g., of heart monitoring device 202 a and/or gateway device229 a) may include a plurality of Siamese branches. Additionally oralternatively, each respective Siamese branch may be associated with arespective ECG channel (e.g., of the plurality of ECG channels). In someembodiments, the neural network(s) of the rhythm change classifier mayfurther include at least one further layer connected to the plurality ofSiamese branches. In some non-limiting embodiments, each Siamese branchof the plurality of Siamese branches may include a plurality ofconvolutional layers. Additionally or alternatively, dimensions of eachof the plurality of convolutional layers of each respective Siamesebranch may be the same as the dimensions of each of the plurality ofconvolutional layers of each other Siamese branch (e.g., convolutionallayers of all Siamese branches may have the same dimensions).

In some embodiments, heart monitoring device 202 a and/or gateway device229 a (e.g., processor(s) thereof) may be further configured to detect(e.g., with the trained rhythm change classifier) the predeterminedrhythm change based on the at least one ECG signal. In some embodiments,heart monitoring device 202 a may further include at least one sensorand associated sensor circuitry configured to sense non-ECG biometricdata of the patient (which, in some embodiments, may be communicated togateway device 229 a). Additionally or alternatively, detecting thepredetermined rhythm change may be further based on the non-ECGbiometric data of the patient (e.g., non-ECG biometric data of thepatient may be input into the neural network(s) of the rhythm changeclassifier, may be combined with the output of the rhythm changeclassifier, and/or the like). In some embodiments, the at least onesensor may include at least one of an accelerometer, a heart sounddetector, a receiver for electromagnetic (e.g., RF) waves (e.g., antennaand/or the like), any combination thereof, and/or the like. Additionallyor alternatively, the non-ECG biometric data may include at least one ofacceleration data, heart sound data, electromagnetic (e.g., RF) wavesscattered and/or reflected from internal tissues, any combinationthereof, and/or the like.

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one baseline ECG signal portion of thepatient. For example, the baseline ECG signal portion(s) may be obtained(e.g., measured, recorded, stored, and/or the like) using high accuracyECG measurements (e.g., from an ECG device separate from the heartmonitoring device 202 a, such as a 12-lead ECG sensor and/or the like)when a patient visits a clinic. Additionally or alternatively, thebaseline ECG signal portion(s) may be obtained (e.g., measured,recorded, stored, and/or the like) using heart monitoring device 202 a(and/or gateway device 229 a) during time periods of known and/orexpected rest (e.g., night hours and/or the like). Additionally oralternatively, the baseline ECG signal portion(s) may be obtained (e.g.,measured, recorded, stored, and/or the like) using heart monitoringdevice 202 a (and/or gateway device 229 a) during time periods of normalsinus rhythm.

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one calibration measurement of the patient.For example, the calibration measurement may be based on at least onesecond ECG signal from second surface ECG activity sensed by a secondplurality of ECG electrodes, which may be independent of the pluralityof ECG electrodes of (external) heart monitoring device 202 a. Forexample, the calibration measurement may be obtained (e.g., measured,recorded, stored, and/or the like) using high accuracy ECG measurements(e.g., from an ECG device separate from the heart monitoring device 202a, such as a 12-lead ECG sensor and/or the like) when a patient visits aclinic.

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one reference vector of the patient. Forexample, the reference vector may include a vector determined (e.g.,calculated and/or the like) based on an aggregation of a plurality ofpast measurements (e.g., past ECG signal portions and/or the like). Insome embodiments, heart monitoring device 202 a (and/or gateway device229 a) may determine the reference vector with a reference-extractionneural network based on the past measurements (e.g., past ECG signalportions and/or the like).

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one previous ECG signal portion. For example,at least one vector may be determined (e.g., calculated and/or the like)based on an aggregation of a plurality of previous ECG signal portions(e.g., from at least one previous time period). In some embodiments, aplurality of vectors may be determined (e.g., calculated and/or thelike) based on an aggregation of a plurality of previous ECG signalportions from each of a plurality of previous time periods (e.g., aprevious day, a previous week, a previous month, and/or the like). Insome embodiments, each vector may be determined (e.g., by heartmonitoring device 202 a and/or gateway device 229 a) with areference-extraction neural network based on the past measurements(e.g., past ECG signal portions and/or the like).

In some embodiments, a gateway device 229 a may enable communicationbetween heart monitoring device 202 a and remote computer system 204 a,as described herein. For example, remote computer system 204 a maycommunicate the trained rhythm change classifier to gateway device 229a. Additionally or alternatively, gateway device 229 a may store thetrained rhythm change classifier and/or communicate the trained rhythmchange classifier to heart monitoring device 202 a. In some embodiments,gateway device 229 a may store the trained rhythm change classifier(and/or a plurality of weights corresponding thereto). Additionally oralternatively, gateway device 229 a may be configured to use the trainedrhythm change classifier to perform the rhythm change classification, asdescribed herein. For example, gateway device 229 a may be configured toreceive the ECG signal(s) from heart monitoring device 202 a (e.g.,continuously, periodically, and/or the like), process the ECG signal(s)from heart monitoring device 202 a based on the trained rhythm changeclassifier, and/or communicate (e.g., transmit and/or the like)determined/identified ECG signal portion(s) corresponding to thepredetermined rhythm change rhythm change(s) to remote computer system204 a.

In some embodiments, the processor (e.g., of heart monitoring device 202a) may further determine (e.g., with the rhythm change classifier) aconfidence score associated with the predetermined rhythm change basedon the at least one ECG signal. For example, an output of at least oneneural network of the rhythm change classifier may include theconfidence score (e.g., a probability that the determined ECG signalportion(s) contain a predetermined rhythm change and/or the like). Insome embodiments, the rhythm change classifier may include a pluralityof neural networks, and each such neural network may output a predictionvalue associated with at least one predetermined rhythm change.Additionally or alternatively, such outputs may be combined (e.g.,aggregated and/or the like) and/or the confidence score may becalculated based on such outputs.

As shown in FIG. 2A, at 234 a, heart monitoring device 202 a and/orgateway device 229 a (e.g., the processor(s) thereof) may communicate(e.g., transmit and/or the like) the determined ECG signal portion(s) toremote computer system 204 a, as described herein. Additionally oralternatively, heart monitoring device 202 a and/or gateway device 229 a(e.g., the processor(s) thereof) may be configured to detect and/orcommunicate an indication (e.g., a flag, an indicator, a confidencescore, a mark, metadata, the time data, and/or the like) associated withthe predetermined rhythm change detected (e.g., identified and/or thelike) in the ECG signal portion(s).

In some embodiments, the processor (e.g., of heart monitoring device 202a and/or gateway device 229 a) may further communicate (e.g., transmitand/or the like) at least one second ECG signal portion of the ECGsignal(s) to remote computer system 204 a. Additionally oralternatively, the second ECG signal portion(s) may be independent ofthe detected time data corresponding to the predetermined rhythm changein the ECG signal(s). In some embodiments, the processor (e.g., of heartmonitoring device 202 a and/or gateway device 229 a) may randomlydetermine the second ECG signal portion(s) (e.g., randomly sample theECG signal(s) to determine the second ECG signal portion(s)). In someembodiments, the processor (e.g., of heart monitoring device 202 aand/or gateway device 229 a) may determine, with the rhythm changeclassifier, a first confidence score associated with the predeterminedrhythm change based on the ECG signal(s), and the first confidence scoremay be above a first threshold. Additionally or alternatively, theprocessor (e.g., of heart monitoring device 202 a and/or gateway device229 a) may detect, with the rhythm change classifier, second time datacorresponding to a potential rhythm change in the ECG signal(s) (e.g.,the second time data may include at least one of a second start time, asecond time interval, any combination thereof, and/or the like).Additionally or alternatively, the processor (e.g., of heart monitoringdevice 202 a and/or gateway device 229 a) may determine, with the rhythmchange classifier, a second confidence score associated with thepotential rhythm change based on the ECG signal(s), and the secondconfidence score may be below the first threshold and above a secondthreshold. Additionally or alternatively, the processor (e.g., of heartmonitoring device 202 a and/or gateway device 229 a) may determine,based on the detected second time data, the second ECG signal portion(s)associated with the detected second time data corresponding to thepotential rhythm change in the ECG signal(s).

In some embodiments, gateway device 229 a may enable communicationbetween heart monitoring device 202 a and remote computer system 204 a,as described herein. For example, transmitting the determined ECG signalportion(s) to remote computer system 204 a may include heart monitoringdevice 202 a communicating (e.g., transmitting and/or the like) thedetermined ECG signal portion(s) to gateway device 229 a. Additionallyor alternatively, gateway device 229 a may be configured to receive thedetermined ECG signal portion(s) from heart monitoring device 202 aand/or communicate the determined ECG signal portion(s) to remote server204 a.

In some embodiments, remote computing system 204 a may receive thedetermined ECG signal portion(s) (e.g., from heart monitoring device 202a and/or gateway device 229 a). Additionally or alternatively, remotecomputing system 204 a may analyze the determined ECG signal portion(s)to classify a type of arrhythmia for the rhythm change(s) in the ECGsignal(s). In some embodiments, the type of arrhythmia may include atleast one of a change in heart rate, atrial fibrillation, flutter,supraventricular tachycardia, ventricular tachycardia, pause, AV block,ventricular fibrillation, bigeminy, trigeminy, ventricular ectopicbeats, bradycardia, tachycardia, a change in morphology of the at leastone ECG signal, any combination thereof, and/or the like.

In some embodiments, remote computer system 204 a may include anarrhythmia type classifier (e.g., including at least one (second) neuralnetwork trained based on a (second) historical collection of a (second)plurality of ECG signal portions with known arrhythmia typeinformation), as described herein. Additionally or alternatively,analyzing the at least one determined ECG signal portion may includeremote computer system 204 a detecting with the arrhythmia typeclassifier the type of arrhythmia associated with the rhythm changebased on the determined ECG signal portion(s).

In some embodiments, onset of bradycardia may include a patient's heartrate dropping below a first threshold for the patient. For example, thefirst threshold may include a value of at least 20 beats per minute(BPM) and up to 100 BPM, a value of at least 30 BPM and up to 100 BPM,and/or the like, which may be calculated over a predetermined interval(e.g., a number of heartbeats, such as 16 beats and/or the like). Forexample, a default first threshold may include 40 BPM, and the firstthreshold may be adjusted for each individual patient (e.g., by aprescriber, treating physician, and/or the like). In some embodiments,offset of bradycardia may include a patient's heart rate rising above asecond threshold for the patient. For example, the second threshold mayinclude a value of at least 20 beats per minute (BPM) and up to 100 BPM,which may be calculated over a predetermined interval (e.g., a number ofheart beats, such as 16 beats and/or the like). For example, a defaultsecond threshold may include 45 BPM, and the second threshold may beadjusted for each individual patient (e.g., by a prescriber, treatingphysician, and/or the like). In some embodiments, a patient must remainin bradycardia for a first selected time period before the rhythm changeis reported (e.g., before the ECG signal portion(s) are classified asbradycardia, before any messages associated with the ECG signalportion(s) are communicated, and/or the like). For example, the firstselected time period may include a value of at least 0 seconds and up to600 seconds, a value of at least 15 second and up to 600 seconds, and/orthe like. For example, a default first time period may include 30seconds, and the first time period may be adjusted for each individualpatient (e.g., by a prescriber, treating physician, and/or the like).

In some embodiments, onset of tachycardia may include a patient's heartrate rising above a third threshold for the patient. For example, thethird threshold may include a value of at least 100 BPM and up to 250BPM, a value of at least 100 BPM and up to 249 BPM, and/or the like,which may be calculated over a predetermined interval (e.g., a number ofheartbeats, such as 16 beats and/or the like). For example, a defaultthird threshold may include 1300 BPM, and the third threshold may beadjusted for each individual patient (e.g., by a prescriber, treatingphysician, and/or the like). In some embodiments, offset of tachycardiamay include a patient's heart rate dropping below a fourth threshold forthe patient. For example, the fourth threshold may include a value of atleast 100 BPM and up to 250 BPM, a value of at least 100 BPM and up to249 BPM, and/or the like, which may be calculated over a predeterminedinterval (e.g., a number of heart beats, such as 16 beats and/or thelike). For example, a default fourth threshold may include 110 BPM, andthe fourth threshold may be adjusted for each individual patient (e.g.,by a prescriber, treating physician, and/or the like). In someembodiments, a patient must remain in tachycardia for a second selectedtime period before the rhythm change is reported (e.g., before the ECGsignal portion(s) are classified as tachycardia, before any messagesassociated with the ECG signal portion(s) are communicated, and/or thelike). For example, the second selected time period may include a valueof at least 0 seconds and up to 600 seconds, a value of at least 15second and up to 600 seconds, and/or the like. For example, a defaultsecond time period may include 30 seconds, and the second time periodmay be adjusted for each individual patient (e.g., by a prescriber,treating physician, and/or the like).

In some embodiments, onset of atrial fibrillation may include aquivering or irregular heartbeat of a patient. In some embodiments, apatient must remain in atrial fibrillation for a third selected timeperiod before the rhythm change is reported (e.g., before the ECG signalportion(s) are classified as atrial fibrillation, before any messagesassociated with the ECG signal portion(s) are communicated, and/or thelike). For example, the third selected time period may include a valueof at least 0 minutes and up to 60 minutes and/or the like. For example,a default third time period may include 5 minutes, and the third timeperiod may be adjusted for each individual patient (e.g., by aprescriber, treating physician, and/or the like).

In some embodiments, onset of cardiac pause may include a prolonged R-Rinterval that represents the interruption in ventricular depolarization.In some embodiments, a patient must remain in cardiac pause for a fourthselected time period before the rhythm change is reported (e.g., beforethe ECG signal portion(s) are classified as cardiac pause, before anymessages associated with the ECG signal portion(s) are communicated,and/or the like). For example, the fourth selected time period mayinclude a value of at least 1500 milliseconds (ms) and up to 15000 msand/or the like. For example, a default fourth time period may include3000 ms, and the fourth time period may be adjusted for each individualpatient (e.g., by a prescriber, treating physician, and/or the like).

In some embodiments, onset of bradycardia rate change may include apatient's heart rate dropping at least a first predetermined value belowthe first threshold. In some embodiments, each time the patient's heartrate drops by at least an integer multiple of the first predeterminedvalue, the rhythm change may be reported (e.g., the ECG signalportion(s) may be classified as bradycardia rate change, messagesassociated with the ECG signal portion(s) may be communicated, and/orthe like). For example, the first predetermined value may include avalue of at least 0 BPM and up to 100 BPM and/or the like. For example,a default first predetermined value may 5 BPM, and the firstpredetermined value may be adjusted for each individual patient (e.g.,by a prescriber, treating physician, and/or the like).

In some embodiments, onset of tachycardia rate change may include apatient's heart rate rising at least a second predetermined value abovethe third threshold. In some embodiments, each time the patient's heartrate rises by at least an integer multiple of the second predeterminedvalue, the rhythm change may be reported (e.g., the ECG signalportion(s) may be classified as tachycardia rate change, messagesassociated with the ECG signal portion(s) may be communicated, and/orthe like). For example, the second predetermined value may include avalue of at least 0 BPM and up to 250 BPM and/or the like. For example,a default first predetermined value may 10 BPM, and the secondpredetermined value may be adjusted for each individual patient (e.g.,by a prescriber, treating physician, and/or the like).

In some embodiments, remote computer system 204 a may analyze thedetermined ECG signal portion(s) to identify at least one arrhythmiaassociated with the rhythm change in the at least one ECG signal. Insome embodiments, the arrhythmia may be one or more rare arrhythmias onwhich the remote computer system 204 a may have previously been trained.Additionally or alternatively, remote computer system 204 a may use anysuitable signal processing technique (e.g., separate from or includingthe arrhythmia type classifier as described herein) to identify the rarearrhythmia(s). For example, such a small portion of the historicalcollection of ECG signal portions may be associated with the rarearrhythmia(s) that the arrhythmia type classifier may not besufficiently trained to classify such rare arrhythmia(s). Additionallyor alternatively, remote computer system 204 a may use signal processingtechniques, predetermined rules, and/or the like to identify such rarearrhythmia(s).

In some embodiments, the determined ECG signal portion(s) may include aplurality of determined ECG signal portions. Additionally oralternatively, remote computer system 204 a may receive the plurality ofdetermined ECG signal portions from heart monitoring device 202 a and/orgateway device 229 a, as described herein. In some embodiments, remotecomputer system 204 a may analyze each respective determined ECG signalportion to classify a respective class for each respective determinedECG signal potion. Additionally or alternatively, the class for at leasttwo respective determined ECG signal potions may include a first class(e.g., at least two ECG signal portions may belong to the sameclass/grouping). In some embodiments, analyzing each respectivedetermined ECG signal portion may include determining (e.g., calculatingand/or the like) a vector for each respective determined ECG signalportion. Additionally or alternatively, the vectors may be classifiedinto classes (e.g., groups, clusters, and/or the like) based onsimilarity between the respective vectors (e.g., vector distance,clustering, and/or the like).

As shown in FIG. 2A, at 236 a, remote computer system 204 a maycommunicate (e.g., transmit and/or the like) at least one messageassociated with the determined ECG signal portion(s) and/or the type ofarrhythmia associated with the rhythm change, as described herein. Forexample, the message(s) may be communicated from remote computer system204 a to technician device 208 a, as described herein.

In some embodiments, remote computer system 204 a may transmit at leastone message associated with the second ECG signal portion(s) (e.g.,randomly determined second ECG signal portion(s), second ECG signalportion(s) determined to have a confidence score below a first thresholdand above a second threshold, and/or the like, as described herein) totechnician device 208 a.

In some embodiments, remote computer system 204 a may transmit at leastone message associated with the at least two respective determined ECGsignal portions and the first class to technician device 208 a. Forexample, a technician using technician device 208 a may be able toreview the at least two respective determined ECG signal portions moreefficiently, rapidly, and/or the like, since similar ECG signal portionsare grouped together in the same class (e.g., the first class).

As shown in FIG. 2A, at 238 a, remote computer system 204 a may receiveannotation data associated with at least one annotation from techniciandevice 208 a, as described herein. For example, the annotation data maybe communicated from technician device 208 a to remote computer system204 a, as described herein. Additionally or alternatively, the ECGsignal portion(s) associated with such annotation(s) may be communicatedwith the annotation data, as described herein.

In some embodiments, remote computer system 204 a may receive (e.g.,from technician device 208 a) annotation data associated with at leastone annotation for the second ECG signal portion(s) (e.g., randomlydetermined second ECG signal portion(s), second ECG signal portion(s)determined to have a confidence score below a first threshold and abovea second threshold, and/or the like, as described herein). In someembodiments, remote computer system 204 a may retrain the rhythm changeclassifier (and/or train an updated rhythm change classifier) based onthe historical collection of the plurality of ECG signal portions withthe known rhythm change information, the second ECG signal portion(s),and the annotation data associated therewith. In some embodiments,remote computer system 204 a may retrain the arrhythmia type classifierbased on the historical collection of the plurality of ECG signalportions with the known rhythm change information, the second ECG signalportion(s), and the annotation data associated therewith.

As shown in FIG. 2A, at 240 a, remote computer system 204 a maycommunicate (e.g., transmit, write, and/or the like) the annotation datato data repository 206 a, as described herein. Additionally oralternatively, the ECG signal portion(s) associated with suchannotation(s) may be communicated with the annotation data, as describedherein.

In some embodiments, annotation data and the ECG signal portion(s)associated with such annotation(s) may be added to the historicalcollection of the plurality of ECG signal portions. For example, theannotation data may be stored as the known rhythm change informationand/or the known arrhythmia type information for the ECG signalportion(s) associated with such annotation(s). In some embodiments,remote computer system 204 a may retrain the rhythm change classifier(and/or train an updated rhythm change classifier) based on thehistorical collection of the plurality of ECG signal portions with theknown rhythm change information (which may now include the ECG signalportion(s) and/or the annotation data associated therewith). In someembodiments, remote computer system 204 a may retrain the arrhythmiatype classifier based on the historical collection of the plurality ofECG signal portions with the known arrhythmia type information (whichmay now include the ECG signal portion(s) and/or the annotation dataassociated therewith).

In some embodiments, remote computer system 204 a may add annotationdata associated with at least one annotation for the second ECG signalportion(s) (e.g., randomly determined second ECG signal portion(s),second ECG signal portion(s) determined to have a confidence score belowa first threshold and above a second threshold, and/or the like, asdescribed herein) to the historical collection of the plurality of ECGsignal portions in data repository 206 a. In some embodiments, remotecomputer system 204 a may retrain the rhythm change classifier (and/ortrain an updated rhythm change classifier) based on the historicalcollection of the plurality of ECG signal portions with the known rhythmchange information (which may now include the second ECG signalportion(s) and/or the annotation data associated therewith). In someembodiments, remote computer system 204 a may retrain the arrhythmiatype classifier based on the historical collection of the plurality ofECG signal portions with the known arrhythmia type information (whichmay now include the second ECG signal portion(s) and/or the annotationdata associated therewith).

As shown in FIG. 2A, at 242 a, remote computer system 204 a maycommunicate the retrained rhythm change classifier (and/or trainedupdated rhythm change classifier) to heart monitoring device 202 aand/or gateway device 229 a, as described herein. Additionally oralternatively, a copy of the retrained rhythm change classifier (and/ortrained updated rhythm change classifier) may be downloaded from remotecomputer system 204 a and/or installed on (e.g., uploaded to, writtento, configured on, and/or the like) at least one non-transitory computerreadable medium (e.g., e.g., memory, programmable circuit board, FPGA,integrated circuit, any combination thereof, and/or the like), which maybe installed in and/or part of heart monitoring device 202 a and/orgateway device 229 a.

Referring now to FIG. 2B, FIG. 2B shows an example block diagram of asystem architecture 200 b for arrhythmia monitoring, according to someembodiments. In addition to system components, the system architecture200 b also shows data flows between the system components. As shown inFIG. 2B, system architecture 200 b may include heart monitoring device202 b, remote computer system 204 b, data repository 206 b, techniciandevice 208 b, and/or gateway device 229 b. In some embodiments, heartmonitoring device 202 b may be the same as or similar to heartmonitoring device 102, heart monitoring device 202 a, and/or the like.In some embodiments, remote computer system 204 b may be the same as orsimilar to remote computer system 104 (e.g., one or more devices ofremote computer system 104), remote computer system 204 a (e.g., one ormore devices of remote computer system 204 a), and/or the like. In someembodiments, data repository 206 b may be the same as or similar to datarepository 106 (e.g., one or more devices of data repository 106), datarepository 206 a (e.g., one or more devices of data repository 206 a),and/or the like. In some embodiments, technician device 208 b may be thesame as or similar to technician device 108, technician device 208 a,and/or the like. In some embodiments, gateway device 229 b may be thesame as or similar to gateway device 129.

As shown in FIG. 2B, at 230 b, remote computer system 204 b may receive(e.g., retrieve, search for, send a request and/or query to cause datarepository 206 b to communicate, and/or the like) a historicalcollection of a plurality of ECG signal portions and information relatedthereto (e.g., known rhythm change information, known arrhythmia typeinformation, and/or the like), e.g., from data repository 206 b, asdescribed herein. In some embodiments, remote computer system 204 b maytrain at least one neural network of at least one classifier (e.g., anarrhythmia type classifier and/or the like) based on the historicalcollection of a plurality of ECG signal portions and information relatedthereto (e.g., known arrhythmia type information and/or the like), asdescribed herein.

In some embodiments, an arrhythmia type classifier may include at leastone neural network (e.g., at least one second neural network), asdescribed herein. Additionally or alternatively, the at least one(second) neural network may include at least one of a deep neuralnetwork, a convolutional neural network, a recurrent neural network, anattention network, a fully connected neural network, any combinationthereof, and/or the like. For example, the neural network(s) may includeat least one convolutional neural network having a plurality ofconvolutional layers. In some embodiments, the convolutional neuralnetwork(s) may include between five and 40 convolutional layers (e.g.,at least five convolutional layers and up to 40 convolutional layers).For example, the convolutional neural network(s) may include betweenseven and ten convolutional layers (e.g., at least seven convolutionallayers and no more than ten convolutional layers). In some embodiments,each convolutional layer may include at least one convolutional nodes(e.g., a plurality of convolutional nodes). In some embodiments, theconvolutional neural network(s) may further include an input layer andan output layer. For example, the ECG signal(s) may include a pluralityof ECG signal samples. Additionally or alternatively, the input layermay include at least one node for each ECG signal sample of theplurality of ECG signal samples (or a subset thereof, e.g., associatedwith a predetermined time period, a buffer size of a buffer for ECGsignal samples, and/or the like). Additionally or alternatively, theinput layer may further include at least one input for non-ECG biometricdata associated with sensors (e.g., of heart monitoring device 202and/or the like), as described herein. In some embodiments, an output ofthe output layer may include an indication of the time datacorresponding to the arrhythmia type. Additionally or alternatively, anoutput of the output layer may include a confidence score, aplausibility score, and/or the lie, as described herein. In someembodiments, the neural network(s) may include a plurality of Siamesebranches (e.g., each respective Siamese branch associated with arespective ECG channel), as described herein.

In some embodiments, remote computer system 204 b may train anarrhythmia type classifier by generating, with the arrhythmia typeclassifier, a predicted type of arrhythmia in each respective ECG signalportion of the historical collection of the plurality of ECG signalportions (or a second plurality thereof), determining at least one errorvalue based on the predicted type of arrhythmia and the known arrhythmiatype information (e.g., a respective annotations associated with a knowntype of arrhythmia for each respective ECG signal portion), and updatingthe arrhythmia type classifier (e.g., updating the weights thereofand/or the like) based on the error value(s) (e.g., using backpropagation and/or the like). In some embodiments, the error value(s)may include one of a prediction error or a contrastive loss.

As shown in FIG. 2B, at 232 b, remote computer system 204 b maycommunicate a historical collection of a plurality of ECG signalportions and information related thereto (e.g., known rhythm changeinformation and/or the like) to heart monitoring device 202 b and/orgateway device 229 b, as described herein. Additionally oralternatively, heart monitoring device 202 b and/or gateway device 229 bmay train a rhythm change classifier, which may be implemented by atleast one non-transitory computer readable medium (e.g., e.g., a memory,a programmable circuit board, a field programmable gate array (FPGA), anintegrated circuit, any combination thereof, and/or the like) that maybe installed in and/or part of heart monitoring device 202 b and/orgateway device 229 b, as described herein.

In some embodiments, heart monitoring device 202 b may be an externalheart monitoring device for a patient, as described herein. For example,(external) heart monitoring device 202 b may include a plurality of ECGelectrodes configured to sense surface ECG activity of the patient.Additionally or alternatively, (external) heart monitoring device 202 bmay include ECG processing circuitry configured to process the surfaceECG activity of the patient to provide at least one ECG signal for thepatient on at least one ECG channel.

In some embodiments, (external) heart monitoring device 202 b mayinclude a non-transitory computer-readable medium (e.g., a memory, aprogrammable circuit board, a field programmable gate array, anintegrated circuit, any combination thereof, and/or the like) including(e.g., implementing, embodying, storing, and/or the like) the rhythmchange classifier (which may include, e.g., at least one neuralnetwork), as described herein. Additionally or alternatively, (external)heart monitoring device 202 b may include at least one processoroperatively connected to the ECG channel(s) and the non-transitorycomputer-readable medium.

In some embodiments, gateway device 229 b may include a non-transitorycomputer-readable medium (e.g., a memory, a programmable circuit board,a field programmable gate array, an integrated circuit, any combinationthereof, and/or the like) including (e.g., implementing, embodying,storing, and/or the like) the rhythm change classifier (which mayinclude, e.g., at least one neural network), as described herein.Additionally or alternatively, gateway device 229 b may include at leastone processor operatively connected to the non-transitorycomputer-readable medium.

In some embodiments, a rhythm change classifier may include at least oneneural network, as described herein. Additionally or alternatively, theat least one neural network may include at least one of a convolutionalneural network, a recurrent neural network, an attention network, afully connected neural network, any combination thereof, and/or thelike, as described herein. For example, the neural network(s) mayinclude at least one convolutional neural network having a plurality ofconvolutional layers. In some embodiments, the convolutional neuralnetwork(s) may include between five and 40 convolutional layers (e.g.,at least five convolutional layers and up to 40 convolutional layers).For example, the convolutional neural network(s) may include betweenseven and ten convolutional layers (e.g., at least seven convolutionallayers and no more than ten convolutional layers). In some embodiments,each convolutional layer may include at least one convolutional nodes(e.g., a plurality of convolutional nodes). In some embodiments, theconvolutional neural network(s) may further include an input layer andan output layer. For example, the ECG signal(s) may include a pluralityof ECG signal samples. Additionally or alternatively, the input layermay include at least one node for each ECG signal sample of theplurality of ECG signal samples (or a subset thereof, e.g., associatedwith a predetermined time period, a buffer size of a buffer for ECGsignal samples, and/or the like). Additionally or alternatively, theinput layer may further include at least one input for non-ECG biometricdata associated with each sensor of heart monitoring device 202 b, asdescribed herein. In some embodiments, an output of the output layer mayinclude an indication of the time data corresponding to the rhythmchange. Additionally or alternatively, an output of the output layer mayinclude a confidence score, as described herein. In some embodiments,the neural network(s) may include a plurality of Siamese branches (e.g.,each respective Siamese branch associated with a respective ECGchannel), as described herein.

In some embodiments, heart monitoring device 202 b and/or gateway device229 b may train the rhythm change classifier by generating, with therhythm change classifier, predicted rhythm change information (e.g.,data (e.g., probability, confidence score, and/or the like) associatedwith a predicted rhythm change, data (e.g., probability, confidencescore, and/or the like) associated with a lack of a predicted rhythmchange, and/or the like) for each ECG signal portion of the historicalcollection of the plurality of ECG signal portions, determining at leastone error value based on the predicted rhythm change information and theknown rhythm change information, and updating the rhythm changeclassifier (e.g., updating the weights thereof and/or the like) based onthe error value(s) (e.g., using back propagation and/or the like). Insome embodiments, the error value(s) may include one of a predictionerror or a contrastive loss.

In some embodiments, the historical collection of the plurality of ECGsignal portions may include a first ECG signal portion associated with afirst time and a second ECG signal portion associated with a second timeafter the first time. Additionally or alternatively, heart monitoringdevice 202 b and/or gateway device 229 b may train the rhythm changeclassifier by generating, with the rhythm change classifier, a predictedECG signal portion associated with the second time based on the firstECG signal portion, determining at least one error value based on thepredicted ECG signal portion and the second ECG signal portion, andupdatingthe rhythm change classifier (e.g., updating the weights thereofand/or the like) based on the error value(s) (e.g., using backpropagation and/or the like). In some embodiments, the error value(s)may include one of a prediction error or a contrastive loss.

In some embodiments, the historical collection of the plurality of ECGsignal portions may include a first ECG signal portion associated with afirst time and a second ECG signal portion associated with a secondtime. Additionally or alternatively, heart monitoring device 202 band/or gateway device 229 b may train the rhythm change classifier bygenerating, with the rhythm change classifier, a predicted timeassociated with the second ECG signal portion based on the a first ECGsignal portion and the second ECG signal, determining at least one errorvalue based on the predicted time and the second time, and updating therhythm change classifier (e.g., updating the weights thereof and/or thelike) based on the error value(s) (e.g., using back propagation and/orthe like). In some embodiments, the error value(s) may include one of aprediction error or a contrastive loss.

In some embodiments, there may be an insufficient number of ECG signalportions in the historical collection of the plurality of ECG signalportions with known rhythm change information to train the rhythm changeclassifier to perform the desired task (e.g., detect and/or identify atleast one predetermined rhythm changes). Additionally or alternatively,there may be sufficient data (e.g., historical ECG signal portionsand/or the like) to train the rhythm change classifier to perform aseparate task (e.g., which may be related in some way to the targettask). In some embodiments, the rhythm change classifier may be trainedto perform the separate task (e.g., counting R-peaks, determining heartrate, and/or the like based on the ECG signal(s)). Additionally oralternatively, the rhythm change classifier may then be adapted toperform the target task. For example, in some embodiments, the rhythmchange classifier may be retrained using the limited amount of ECGsignal portions in the historical collection of the plurality of ECGsignal portions with known rhythm change information and/or the like.Additionally or alternatively, the rhythm change classifier may be usedto perform the separate task (e.g., counting R-peaks, determining heartrate, and/or the like based on the ECG signal(s)), and the outputthereof may be applied to the target task. For example, the processor(e.g., of heart monitoring device 202 b and/or gateway device 229 b) maydetect, with the rhythm change classifier, at least one of a count ofpeaks or a heart rate based on the at least one ECG signal. Additionallyor alternatively, the processor (e.g., of heart monitoring device 202 band/or gateway device 229 b) may determine the detected at least one ofthe count of peaks or the heart rate is above a first threshold (e.g.,tachycardia onset threshold) for the patient or below a second threshold(e.g., bradycardia onset threshold) for the patient (e.g., wherein thesecond threshold for the patient may be less than the first thresholdfor the patient). Additionally or alternatively, the processor (e.g., ofheart monitoring device 202 b and/or gateway device 229 b) may detectthe predetermined rhythm change based on the at least one of the countof peaks or the heart rate being above the first threshold (e.g.,tachycardia onset threshold) for the patient or below the secondthreshold (e.g., bradycardia onset threshold) for the patient.

In some embodiments, there may be an insufficient number of ECG signalportions associated with (e.g., sensed from and/or the like) theplurality of ECG electrodes of the heart monitoring device 202 b in thehistorical collection of the plurality of ECG signal portions with knownrhythm change information to train the rhythm change classifier for theECG signal(s) received form the plurality of ECG electrodes of the heartmonitoring device 202 a. Additionally or alternatively, there may besufficient data (e.g., historical ECG signal portions and/or the like)associated with (e.g., sensed from and/or the like) a second pluralityof ECG electrodes (e.g., electrodes from an ECG device separate from theheart monitoring device 202 b, such as a 12-lead ECG sensor, a separateexternal and/or wearable heart monitoring device, and/or the like)independent of the plurality of ECG electrodes of the heart monitoringdevice 202 a to train the rhythm change classifier based on the secondplurality of ECG electrodes. In some embodiments, the rhythm changeclassifier may be trained based on the ECG signal portion(s) associatedwith (e.g., sensed from and/or the like) the second plurality of ECGelectrodes. Additionally or alternatively, the rhythm change classifiermay then be adapted to detect the predetermined rhythm change(s) basedon the plurality of ECG electrodes of heart monitoring device 202 b. Insome embodiments, heart monitoring device 202 b and/or gateway device229 b may determine (e.g., calculate and/or the like) a transform (e.g.,vector projection and/or the like) of the ECG signal portion(s)associated with the second plurality of ECG electrodes to the pluralityof ECG electrodes of heart monitoring device 202 b, and the transform ofthe ECG signal portion(s) may be used to train the rhythm changeclassifier as if the ECG signal portions were associated with (e.g.,sensed from and/or the like) the plurality of ECG electrodes of theheart monitoring device 202 b.

In some embodiments, heart monitoring device 202 b (e.g., theprocessor(s) thereof) may be configured to receive the ECG signal(s) viathe ECG channel(s), as described herein. Additionally or alternatively,heart monitoring device 202 b (e.g., the processor(s) thereof) may beconfigured to detect with the rhythm change classifier time datacorresponding to a predetermined rhythm change in ECG signal(s), asdescribed herein. In some embodiments, heart monitoring device 202 a(e.g., the processor(s) thereof) may be configured to determine based onthe detected time data at least one ECG signal portion associated withthe detected time data corresponding to the predetermined rhythm changein the ECG signal(s), as described herein.

In some embodiments, heart monitoring device 202 b (e.g., theprocessor(s) thereof) may be configured to receive the ECG signal(s) viathe ECG channel(s), as described herein. Additionally or alternatively,heart monitoring device 202 b (e.g., the processor(s) thereof) may beconfigured to transmit the ECG signal(s) to gateway device 229 b. Insome embodiments, gateway device 229 b may be configured to detect withthe rhythm change classifier time data corresponding to a predeterminedrhythm change in ECG signal(s), as described herein. In someembodiments, gateway device 229 b (e.g., the processor(s) thereof) maybe configured to determine based on the detected time data at least oneECG signal portion associated with the detected time data correspondingto the predetermined rhythm change in the ECG signal(s), as describedherein.

In some embodiments, the neural network(s) of the rhythm changeclassifier (e.g., of heart monitoring device 202 b and/or gateway device229 b) may include a plurality of Siamese branches, as described herein.

In some embodiments, heart monitoring device 202 b and/or gateway device229 b (e.g., processor(s) thereof) may be further configured to detect(e.g., with the trained rhythm change classifier) the predeterminedrhythm change based on the at least one ECG signal, as described herein.In some embodiments, heart monitoring device 202 b may further includeat least one sensor and associated sensor circuitry configured to sensenon-ECG biometric data of the patient (which, in some embodiments, maybe communicated to gateway device 229 b), as described herein.Additionally or alternatively, detecting the predetermined rhythm changemay be further based on the non-ECG biometric data of the patient (e.g.,non-ECG biometric data of the patient may be input into the neuralnetwork(s) of the rhythm change classifier, may be combined with theoutput of the rhythm change classifier, and/or the like), as describedherein.

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one baseline ECG signal portion of thepatient, as described herein.

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one calibration measurement of the patient, asdescribed herein.

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one reference vector of the patient, asdescribed herein.

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one previous ECG signal portion, as describedherein.

In some embodiments, gateway device 229 b may enable communicationbetween heart monitoring device 202 b and remote computer system 204 b,as described herein.

In some embodiments, the processor (e.g., of heart monitoring device 202b and/or gateway device 229 b) may further determine (e.g., with therhythm change classifier) a confidence score associated with thepredetermined rhythm change based on the at least one ECG signal, asdescribed herein.

As shown in FIG. 2B, at 234 b, heart monitoring device 202 b and/orgateway device 229 b (e.g., the processor(s) thereof) may communicate(e.g., transmit and/or the like) the determined ECG signal portion(s) toremote computer system 204 b, as described herein. Additionally oralternatively, heart monitoring device 202 b and/or gateway device 229 b(e.g., the processor(s) thereof) may be configured to communicate anindication (e.g., a flag, an indicator, a confidence score, a mark,metadata, the time data, and/or the like) associated with thepredetermined rhythm change detected (e.g., identified and/or the like)in the ECG signal portion(s), as described herein.

In some embodiments, the processor (e.g., of heart monitoring device 202b and/or gateway device 229 b) may further communicate (e.g., transmitand/or the like) at least one second ECG signal portion of the ECGsignal(s) to remote computer system 204 b, as described herein.Additionally or alternatively, the second ECG signal portion(s) may beindependent of the detected time data corresponding to the predeterminedrhythm change in the ECG signal(s), as described herein.

In some embodiments, gateway device 229 b may enable communicationbetween heart monitoring device 202 b and remote computer system 204 b,as described herein.

In some embodiments, remote computing system 204 b may receive thedetermined ECG signal portion(s) (e.g., from heart monitoring device 202b and/or gateway device 229 b), as described herein. Additionally oralternatively, remote computing system 204 b may analyze the determinedECG signal portion(s) to classify a type of arrhythmia for the rhythmchange(s) in the ECG signal(s), as described herein. For example, remotecomputer system 204 b may include an arrhythmia type classifier (e.g.,including at least one (second) neural network trained based on a(second) historical collection of a (second) plurality of ECG signalportions with known arrhythmia type information), as described herein.

In some embodiments, remote computer system 204 b may analyze thedetermined ECG signal portion(s) to identify at least one arrhythmiaassociated with the rhythm change in the at least one ECG signal, asdescribed herein. In some embodiments, the arrhythmia may be one or morerare arrhythmias on which the remote computer system 204 a may havepreviously been trained. Additionally or alternatively, remote computersystem 204 a may use any suitable signal processing technique (e.g.,separate from or including the arrhythmia type classifier as describedherein) to identify the rare arrhythmia(s), as described herein.

In some embodiments, the determined ECG signal portion(s) may include aplurality of determined ECG signal portions, as described herein.Additionally or alternatively, remote computer system 204 b may receivethe plurality of determined ECG signal portions from heart monitoringdevice 202 b and/or gateway device 229 b, as described herein. In someembodiments, remote computer system 204 b may analyze each respectivedetermined ECG signal portion to classify a respective class for eachrespective determined ECG signal potion, as described herein.

As shown in FIG. 2 b , at 236 b, remote computer system 204 b maycommunicate (e.g., transmit and/or the like) at least one messageassociated with the determined ECG signal portion(s) and/or the type ofarrhythmia associated with the rhythm change, as described herein. Forexample, the message(s) may be communicated from remote computer system204 b to technician device 208 b, as described herein.

In some embodiments, remote computer system 204 b may transmit at leastone message associated with the second ECG signal portion(s) (e.g.,randomly determined second ECG signal portion(s), second ECG signalportion(s) determined to have a confidence score below a first thresholdand above a second threshold, and/or the like, as described herein) totechnician device 208 b, as described herein.

In some embodiments, remote computer system 204 b may transmit at leastone message associated with the at least two respective determined ECGsignal portions and the first class to technician device 208 b, asdescribed herein.

As shown in FIG. 2B, at 238 b, remote computer system 204 b may receiveannotation data associated with at least one annotation from techniciandevice 208 a, as described herein. For example, the annotation data maybe communicated from technician device 208 b to remote computer system204 b, as described herein. Additionally or alternatively, the ECGsignal portion(s) associated with such annotation(s) may be communicatedwith the annotation data, as described herein.

In some embodiments, remote computer system 204 b may receive (e.g.,from technician device 208 b) annotation data associated with at leastone annotation for the second ECG signal portion(s) (e.g., randomlydetermined second ECG signal portion(s), second ECG signal portion(s)determined to have a confidence score below a first threshold and abovea second threshold, and/or the like, as described herein).

In some embodiments, remote computer system 204 a may retrain thearrhythmia type classifier based on the historical collection of theplurality of ECG signal portions with the known rhythm changeinformation, the second ECG signal portion(s), and the annotation dataassociated therewith, as described herein.

As shown in FIG. 2B, at 240 b, remote computer system 204 b maycommunicate (e.g., transmit, write, and/or the like) the annotation datato data repository 206 b, as described herein. Additionally oralternatively, the ECG signal portion(s) associated with suchannotation(s) may be communicated with the annotation data, as describedherein.

In some embodiments, annotation data and the ECG signal portion(s)associated with such annotation(s) may be added to the historicalcollection of the plurality of ECG signal portions, as described herein.In some embodiments, remote computer system 204 b may retrain thearrhythmia type classifier based on the historical collection of theplurality of ECG signal portions with the known arrhythmia typeinformation (which may now include the ECG signal portion(s) and/or theannotation data associated therewith).

In some embodiments, remote computer system 204 b may add annotationdata associated with at least one annotation for the second ECG signalportion(s) (e.g., randomly determined second ECG signal portion(s),second ECG signal portion(s) determined to have a confidence score belowa first threshold and above a second threshold, and/or the like, asdescribed herein) to the historical collection of the plurality of ECGsignal portions in data repository 206 b, as described herein. In someembodiments, remote computer system 204 b may retrain the arrhythmiatype classifier based on the historical collection of the plurality ofECG signal portions with the known arrhythmia type information (whichmay now include the second ECG signal portion(s) and/or the annotationdata associated therewith), as described herein.

As shown in FIG. 2B, at 242 b, remote computer system 204 b maycommunicate (e.g., transmit and/or the like) the annotation dataassociated with at least one annotation for the second ECG signalportion(s) to heart monitoring device 202 b and/or gateway device 229 b,as described herein. Additionally or alternatively, heart monitoringdevice 202 b and/or gateway device 229 b (e.g., processor(s) thereof)may retrain the rhythm change classifier (and/or train an updated rhythmchange classifier) based on the historical collection of the pluralityof ECG signal portions with the known rhythm change information, thesecond ECG signal portion(s), and the annotation data associatedtherewith, as described herein.

Referring now to FIG. 2C, FIG. 2C shows an example block diagram of asystem architecture 200 c for arrhythmia monitoring, according to someembodiments. In addition to system components, the system architecture200 c also shows data flows between the system components. As shown inFIG. 2C, system architecture 200 c may include remote computer system204 c, data repository 206 c, technician device 208 c, and/or supervisordevice 210 c. In some embodiments, remote computer system 204 c may bethe same as or similar to remote computer system 104 (e.g., one or moredevices of remote computer system 104), remote computer system 204 a(e.g., one or more devices of remote computer system 204 a), remotecomputer system 204 b (e.g., one or more devices of remote computersystem 204 b), and/or the like. In some embodiments, data repository 206c may be the same as or similar to data repository 106 (e.g., one ormore devices of data repository 106), data repository 206 a (e.g., oneor more devices of data repository 206 a), data repository 206 b (e.g.,one or more devices of data repository 206 b), and/or the like. In someembodiments, technician device 208 c may be the same as or similar totechnician device 108, technician device 208 a, technician device 208 b,and/or the like. In some embodiments, supervisor device 210 c may be thesame as or similar to supervisor device 210 c and/or the like.

As shown in FIG. 2C, at 230 c, remote computer system 204 c may receive(e.g., retrieve, search for, send a request and/or query to cause datarepository 206 c to communicate, and/or the like) a historicalcollection of a plurality of ECG signal portions and information relatedthereto (e.g., known arrhythmia type information and/or the like), e.g.,from data repository 206 c, as described herein. In some embodiments,remote computer system 204 c may train at least one neural network of atleast one classifier (e.g., an arrhythmia type classifier and/or thelike) based on the historical collection of a plurality of ECG signalportions and information related thereto (e.g., known arrhythmia typeinformation and/or the like), as described herein.

In some embodiments, an arrhythmia type classifier may include at leastone neural network (e.g., at least one second neural network), asdescribed herein. Additionally or alternatively, the at least one(second) neural network may include at least one of a deep neuralnetwork, a convolutional neural network, a recurrent neural network, anattention network, a fully connected neural network, any combinationthereof, and/or the like. For example, the neural network(s) may includeat least one convolutional neural network having a plurality ofconvolutional layers. In some embodiments, the convolutional neuralnetwork(s) may include between five and 40 convolutional layers (e.g.,at least five convolutional layers and up to 40 convolutional layers).For example, the convolutional neural network(s) may include betweenseven and ten convolutional layers (e.g., at least seven convolutionallayers and no more than ten convolutional layers). In some embodiments,each convolutional layer may include at least one convolutional nodes(e.g., a plurality of convolutional nodes). In some embodiments, theconvolutional neural network(s) may further include an input layer andan output layer. For example, the ECG signal(s) may include a pluralityof ECG signal samples. Additionally or alternatively, the input layermay include at least one node for each ECG signal sample of theplurality of ECG signal samples (or a subset thereof, e.g., associatedwith a predetermined time period, a buffer size of a buffer for ECGsignal samples, and/or the like). Additionally or alternatively, theinput layer may further include at least one input for non-ECG biometricdata associated with sensors (e.g., of heart monitoring device 102and/or the like), as described herein. In some embodiments, an output ofthe output layer may include an indication of the time datacorresponding to the arrhythmia type. Additionally or alternatively, anoutput of the output layer may include a confidence score, aplausibility score, and/or the lie, as described herein. In someembodiments, the neural network(s) may include a plurality of Siamesebranches (e.g., each respective Siamese branch associated with arespective ECG channel), as described herein.

In some embodiments, remote computer system 204 c may train anarrhythmia type classifier by predicting, with the arrhythmia typeclassifier, a predicted type of arrhythmia in each respective ECG signalportion of the historical collection of the plurality of ECG signalportions (or a second plurality thereof), determining at least one errorvalue based on the predicted type of arrhythmia and the known arrhythmiatype information (e.g., a respective annotations associated with a knowntype of arrhythmia for each respective ECG signal portion), and trainingthe arrhythmia type classifier (e.g., updating the weights thereofand/or the like) based on the error value(s) (e.g., using backpropagation and/or the like). In some embodiments, the error value(s)may include one of a prediction error or a contrastive loss.

In some embodiments, the known arrhythmia type information may include aplurality of annotations. For example, each annotation may be associatedwith a respective ECG signal portion of the plurality of ECG signalportions. In some embodiments, remote computer system 204 c may trainthe arrhythmia type classifier based on the plurality of ECG signals andthe plurality of annotations, as described herein.

In some embodiments, the plurality of annotations may be from aplurality of technicians (e.g., a plurality of technician devices 208 cand/or the like). Additionally or alternatively, each annotationannotations may be associated with a respective technician of theplurality of technicians and/or a respective ECG signal portion of theplurality of ECG signal portions. In some embodiments, the arrhythmiatype classifier may be trained separately for each technician. Forexample, for a first technician of the plurality of technicians, thearrhythmia type classifier may be trained (e.g., by remote computersystem 204 c) based on a subset of the plurality of ECG signals and theplurality of annotations associated with at least one other technicianof the plurality of technicians different than the first technician(e.g., for the first technician, only train the arrhythmia typeclassifier based on annotations from other technicians).

In some embodiments, each annotation may be associated with one possibletype of arrhythmia (e.g., a label associated with a possible type ofarrhythmia, a text string identifying at least one possible typearrhythmia, and/or the like) of the respective ECG signal(s) and/orportion(s) thereof.

In some embodiments, there may be an insufficient number of ECG signalportions associated with (e.g., sensed from and/or the like) at leastone second ECG electrode in the historical collection of the pluralityof ECG signal portions with known arrhythmia type information (e.g.,annotations, labels, and/or the like) to train the arrhythmia typeclassifier for the ECG signal(s) received from the second ECGelectrode(s). Additionally or alternatively, there may be sufficientdata (e.g., historical ECG signal portions and/or the like) associatedwith (e.g., sensed from and/or the like) at least one first ECGelectrode (e.g., electrodes from an ECG device separate from the secondECG electrode(s), such as a 12-lead ECG sensor, a separate externaland/or wearable heart monitoring device, and/or the like) independent ofthe second ECG electrode(s) to train the rhythm change classifier basedon the second ECG electrode(s). In some embodiments, the knownarrhythmia type information may include a plurality of annotations, eachof which may be associated with a respective ECG signal portion of afirst plurality of ECG signal portions associated with the first ECGelectrode(s). In some embodiments, each respective ECG signal portion ofa second plurality of ECG signal portions associated with the second ECGelectrode(s) may correspond to a respective ECG signal portion of thefirst plurality of ECG signal portions. In some embodiments, remotecomputer system 204 c may train the arrhythmia type classifier bypredicting, with the arrhythmia type classifier, a predicted type ofarrhythmia in each respective ECG signal portion of the second pluralityof ECG signal portions, determining at least one error value based onthe predicted type of arrhythmia and the respective annotation of theplurality of annotations associated with a respective ECG signal portionof the first plurality of ECG signal portions corresponding to therespective ECG signal portion of the second plurality of ECG signalportions, and training (e.g., updating the weights of and/or the like)the arrhythmia type classifier based on the at least one error value(e.g., based on back propagation and/or the like).

In some embodiments, ECG signal portions associated with multipleelectrodes may be combined (e.g., by vector addition, vector projection,a transform, and/or the like) to form extrapolated ECG signal portionsthat may be more familiar and/or suitable for review by a human user(e.g., technician and/or the like). For example, the historicalcollection of the plurality of ECG signal portions may include a firstplurality of ECG signal portions of at least one first ECG signal basedon first surface ECG activity sensed by at least one first ECG electrodeand a second plurality of ECG signal portions of at least one second ECGsignal based on second surface ECG activity sensed by at least onesecond ECG electrode. Additionally or alternatively, the at least onesecond ECG electrode may be independent of the at least one first ECGelectrode. In some embodiments, each ECG signal portion of the firstplurality of ECG signal portions may be combined (e.g., by vectoraddition, vector projection, a transform, and/or the like) with arespective ECG signal portion of the second plurality of ECG signalportions to form a plurality of extrapolated ECG signal portions (e.g.,by remote computer system 204 c). In some embodiments, the knownarrhythmia type information may include a plurality of annotations.Additionally or alternatively, each respective annotation may beassociated with a respective extrapolated ECG signal portion of theplurality of extrapolated ECG signal portions.

In some embodiments, at least some of the plurality of ECG signalportions of the historical collection may be time warped (e.g., timedilated and/or the like) to form a plurality of warped ECG signalportions (e.g., by remote computer system 204 c using any suitablesignal processing technique for time warping, time dilation, and/or thelike).

In some embodiments, at least some of the plurality of ECG signalportions of the historical collection may be at least one of filtered,inverted, any combination thereof, and/or the like (e.g., by remotecomputer system 204 c).

In some embodiments, at least one noise signal portion may be combinedwith at least some of the plurality of ECG signal portions of thehistorical collection (e.g., by remote computer system 204 c).

In some embodiments, at least some of the plurality of ECG signalportions of the historical collection may be style transferred (e.g., byremote computer system 204 c). For example, remote computer system 204 cmay search for ECG signals that share low level features with onereference signal and high-level features with a second reference signal(e.g., the second reference signal may be associated with a raretype/class of arrhythmia and/or the like). Additionally oralternatively, the low and/or high level features may be the output of apre-trained classification network with respective low and/or highlayers.

As shown in FIG. 2C, at 232 c, remote computer system 204 c may receiveat least one ECG signal and annotation data associated with at least oneannotation for each ECG signal, as described herein.

In some embodiments, remote computer system 204 c may detect, with thearrhythmia type classifier, a type of arrhythmia in the ECG signal(s)and time data associated with the detected type of arrhythmia, asdescribed herein. For example, the time data may include at least one ofa start time, a time interval, any combination thereof, and/or the like,as described herein. In some embodiments, remote computer system 204 cmay determine, based on the time data, at least one ECG signal portionassociated with the detected type of arrhythmia in the ECG signal(s), asdescribed herein.

In some embodiments, remote computer system 204 c may determine aplausibility score for the annotation(s) based on the detected type ofarrhythmia. For example, an output of at least one neural network of thearrhythmia type classifier may include a confidence score (e.g., aprobability and/or the like) associated with each possible type ofarrhythmia, as described herein. For example, the type of arrhythmiadetermined by the arrhythmia type classifier may be the type ofarrhythmia with a highest confidence score (e.g., probability and/or thelike). Additionally or alternatively, each annotation may be associatedwith one possible type of arrhythmia (e.g., a label associated with apossible type of arrhythmia, a text string identifying at least onepossible type arrhythmia, and/or the like). In some embodiments,plausibility score of each annotation may be the confidence score (e.g.,determined by the arrhythmia type classifier) of the possible type ofarrhythmia associated with such annotation. In some embodiments, thearrhythmia type classifier may include a plurality of neural networks,and each such neural network may output a confidence score associatedwith at least one possible type of arrhythmia.

In some embodiments, remote computer system 204 c may generate at leastone message based on the at least one determined ECG signal portion andthe plausibility score for the at least one annotation, as describedherein. For example, the message(s) may indicate at least one of arecommendation to annotate the at least one determined ECG signalportion based on the detected type of arrhythmia, a recommendation toreevaluate the annotation data associated with the at least onedetermined ECG signal portion based on the plausibility score, and/orthe like.

In some embodiments, remote computer system 204 c may to determine theplausibility score is below a threshold. Additionally or alternatively,generating the message(s) may include remote computer system 204 cgenerating, based on the determination that the plausibility score isbelow the threshold, the at least one message indicating therecommendation to reevaluate the annotation data associated with the atleast one determined ECG signal portion, as described herein.

As shown in FIG. 2C, at 234 c, remote computer system 204 c may transmitthe at least some of the message(s) associated with the at least onedetermined ECG signal portion to technician device 208 c, as describedherein.

As shown in FIG. 2C, at 236 c, remote computer system 204 c may transmitthe at least some of the message(s) associated with the at least onedetermined ECG signal portion to supervisor device 210 c, as describedherein.

Referring now to FIG. 3A, FIG. 3A shows an example swim lane diagram ofa process 300 a for arrhythmia monitoring, according to someembodiments. In addition to system components, the process 300 a alsoshows communication flows between the system components. As shown inFIG. 3A, in some embodiments, heart monitoring device 302 a may be thesame as or similar to heart monitoring device 102, heart monitoringdevices 202 a and/or 202 b, and/or the like. In some embodiments, remotecomputer system 304 a may be the same as or similar to remote computersystem 104, remote computer systems 204 a, 204 b, and/or 204 c, and/orthe like. In some embodiments, data repository 306 a may be the same asor similar to data repository 106, data repositories 206 a, 206 b,and/or 206 c, and/or the like. In some embodiments, technician device308 a may be the same as or similar to technician device 108, techniciandevices 202 a, 202 b, and/or 202 c, and/or the like. In someembodiments, gateway device 329 a may be the same as or similar togateway device 129, gateway devices 229 a and/or 229 b, and/or the like.

As shown in FIG. 3A, at 310 a, remote computer system 304 a may receive(e.g., retrieve, search for, send a request and/or query to cause datarepository 206 a to communicate, and/or the like) a historicalcollection of a plurality of ECG signal portions and information relatedthereto (e.g., known rhythm change information, known arrhythmia typeinformation, and/or the like), e.g., from data repository 306 a, asdescribed herein.

As shown in FIG. 3A, at 310 a, remote computer system 304 a may train atleast one neural network of at least one classifier (e.g., a rhythmchange classifier, an arrhythmia type classifier, and/or the like) basedon the historical collection of a plurality of ECG signal portions andinformation related thereto (e.g., known rhythm change information,known arrhythmia type information, and/or the like, respectively), asdescribed herein.

In some embodiments, a rhythm change classifier may include at least oneneural network, as described herein. For example, the at least oneneural network may include at least one of a convolutional neuralnetwork, a recurrent neural network, an attention network, a fullyconnected neural network, any combination thereof, and/or the like, asdescribed herein.

In some embodiments, remote computer system 304 a may train the rhythmchange classifier by predicting, with the rhythm change classifier,predicted rhythm change information (e.g., data (e.g., probability,confidence score, and/or the like) associated with a predicted rhythmchange, data (e.g., probability, confidence score, and/or the like)associated with a lack of a predicted rhythm change, and/or the like)for each ECG signal portion of the historical collection of theplurality of ECG signal portions, determining at least one error valuebased on the predicted rhythm change information and the known rhythmchange information, and training the rhythm change classifier (e.g.,updating the weights thereof and/or the like) based on the errorvalue(s) (e.g., using back propagation and/or the like), as describedherein. In some embodiments, the error value(s) may include one of aprediction error or a contrastive loss, as described herein.

In some embodiments, the historical collection of the plurality of ECGsignal portions may include a first ECG signal portion associated with afirst time and a second ECG signal portion associated with a second timeafter the first time. Additionally or alternatively, remote computersystem 304 a may train the rhythm change classifier by predicting, withthe rhythm change classifier, a predicted ECG signal portion associatedwith the second time based on the first ECG signal portion, determiningat least one error value based on the predicted ECG signal portion andthe second ECG signal portion, and training the rhythm change classifier(e.g., updating the weights thereof and/or the like) based on the errorvalue(s) (e.g., using back propagation and/or the like), as describedherein. In some embodiments, the error value(s) may include one of aprediction error or a contrastive loss, as described herein.

In some embodiments, the historical collection of the plurality of ECGsignal portions may include a first ECG signal portion associated with afirst time and a second ECG signal portion associated with a secondtime. Additionally or alternatively, remote computer system 304 a maytrain the rhythm change classifier by predicting, with the rhythm changeclassifier, a predicted time associated with the second ECG signalportion based on the a first ECG signal portion and the second ECGsignal, determining at least one error value based on the predicted timeand the second time, and training the rhythm change classifier (e.g.,updating the weights thereof and/or the like) based on the errorvalue(s) (e.g., using back propagation and/or the like), as describedherein. In some embodiments, the error value(s) may include one of aprediction error or a contrastive loss, as described herein.

In some embodiments, remote computer system 304 a may train anarrhythmia type classifier by predicting, with the arrhythmia typeclassifier, a predicted type of arrhythmia in each respective ECG signalportion of the historical collection of the plurality of ECG signalportions (or a second plurality thereof), determining at least one errorvalue based on the predicted type of arrhythmia and the known arrhythmiatype information (e.g., a respective annotations associated with a knowntype of arrhythmia for each respective ECG signal portion), and trainingthe arrhythmia type classifier (e.g., updating the weights thereofand/or the like) based on the error value(s) (e.g., using backpropagation and/or the like), as described herein. In some embodiments,the error value(s) may include one of a prediction error or acontrastive loss, as described herein.

In some embodiments, there may be an insufficient number of ECG signalportions in the historical collection of the plurality of ECG signalportions with known rhythm change information to train the rhythm changeclassifier to perform the desired task (e.g., detect and/or identify atleast one predetermined rhythm changes). Additionally or alternatively,there may be sufficient data (e.g., historical ECG signal portionsand/or the like) to train the rhythm change classifier to perform aseparate task (e.g., which may be related in some way to the targettask). In some embodiments, the rhythm change classifier may be trainedto perform the separate task (e.g., counting R-peaks, determining heartrate, and/or the like based on the ECG signal(s)). Additionally oralternatively, the rhythm change classifier may then be adapted toperform the target task, as described herein. For example, in someembodiments, the rhythm change classifier may be retrained using thelimited amount of ECG signal portions in the historical collection ofthe plurality of ECG signal portions with known rhythm changeinformation and/or the like, as described herein. Additionally oralternatively, the rhythm change classifier may be used to perform theseparate task (e.g., counting R-peaks, determining heart rate, and/orthe like based on the ECG signal(s)), and the output thereof may beapplied to the target task, as described herein. For example, theprocessor (e.g., of heart monitoring device 302 a and/or gateway device329 a) may detect, with the rhythm change classifier, at least one of acount of peaks or a heart rate based on the at least one ECG signal.Additionally or alternatively, the processor (e.g., of heart monitoringdevice 302 a and/or gateway device 329 a) may determine the detected atleast one of the count of peaks or the heart rate is above a firstthreshold (e.g., tachycardia onset threshold) for the patient or below asecond threshold (e.g., bradycardia onset threshold) for the patient(e.g., wherein the second threshold for the patient may be less than thefirst threshold for the patient). Additionally or alternatively, theprocessor (e.g., of heart monitoring device 302 a and/or gateway device329 a) may detect the predetermined rhythm change based on the at leastone of the count of peaks or the heart rate being above the firstthreshold (e.g., tachycardia onset threshold) for the patient or belowthe second threshold (e.g., bradycardia onset threshold) for thepatient.

In some embodiments, there may be an insufficient number of ECG signalportions associated with (e.g., sensed from and/or the like) theplurality of ECG electrodes of the heart monitoring device 302 a in thehistorical collection of the plurality of ECG signal portions with knownrhythm change information to train the rhythm change classifier for theECG signal(s) received form the plurality of ECG electrodes of the heartmonitoring device 202 a. Additionally or alternatively, there may besufficient data (e.g., historical ECG signal portions and/or the like)associated with (e.g., sensed from and/or the like) a second pluralityof ECG electrodes (e.g., electrodes from an ECG device separate from theheart monitoring device 302 a, such as a 12-lead ECG sensor, a separateexternal and/or wearable heart monitoring device, and/or the like)independent of the plurality of ECG electrodes of the heart monitoringdevice 302 a to train the rhythm change classifier based on the secondplurality of ECG electrodes. In some embodiments, the rhythm changeclassifier may be trained based on the ECG signal portion(s) associatedwith (e.g., sensed from and/or the like) the second plurality of ECGelectrodes, as described herein. Additionally or alternatively, therhythm change classifier may then be adapted to detect the predeterminedrhythm change(s) based on the plurality of ECG electrodes of the heartmonitoring device 302 a, as described herein. In some embodiments,remote computer system 204 a may determine (e.g., calculate and/or thelike) a transform (e.g., vector projection and/or the like) of the ECGsignal portion(s) associated with the second plurality of ECG electrodesto the plurality of ECG electrodes of the heart monitoring device 302 a,and the transform of the ECG signal portion(s) may be used to train therhythm change classifier as if the ECG signal portions were associatedwith (e.g., sensed from and/or the like) the plurality of ECG electrodesof the heart monitoring device 302 a.

In some embodiments, an arrhythmia type classifier may include at leastone neural network (e.g., at least one second neural network), asdescribed herein. Additionally or alternatively, the at least one(second) neural network may include at least one of a deep neuralnetwork, a convolutional neural network, a recurrent neural network, anattention network, a fully connected neural network, any combinationthereof, and/or the like, as described herein.

As shown in FIG. 3A, at 314 a, remote computer system 304 a maycommunicate a trained rhythm change classifier (or a plurality ofweights thereof) to heart monitoring device 302 a and/or gateway device329 a, as described herein. In some embodiments, after training, aplurality of weights corresponding to the trained rhythm changeclassifier may be communicated to heart monitoring device 302 a and/orgateway device 329 a. Additionally or alternatively, a copy of thetrained rhythm change classifier (or a plurality of weights thereof) maybe downloaded from remote computer system 304 a and/or installed on(e.g., uploaded to, written to, configured on, and/or the like) at leastone non-transitory computer readable medium (e.g., e.g., a memory, aprogrammable circuit board, a field programmable gate array (FPGA), anintegrated circuit, any combination thereof, and/or the like), which maybe installed in and/or part of heart monitoring device 302 a and/orgateway device 329 a.

In some embodiments, heart monitoring device 302 a may be an externalheart monitoring device for a patient, as described herein. For example,(external) heart monitoring device 302 a may include a plurality of ECGelectrodes configured to sense surface ECG activity of the patient, asdescribed herein. For example, (external) heart monitoring device 302 amay include ECG processing circuitry configured to process the surfaceECG activity of the patient to provide at least one ECG signal for thepatient on at least one ECG channel.

In some embodiments, (external) heart monitoring device 302 a mayinclude a non-transitory computer-readable medium (e.g., a memory, aprogrammable circuit board, a field programmable gate array, anintegrated circuit, any combination thereof, and/or the like) including(e.g., implementing, embodying, storing, and/or the like) the trainedrhythm change classifier (which may include, e.g., at least one neuralnetwork trained based on the historical collection of a plurality of ECGsignal portions with known rhythm change information), as describedherein. Additionally or alternatively, (external) heart monitoringdevice 302 a may include at least one processor operatively connected tothe ECG channel(s) and the non-transitory computer-readable medium.

In some embodiments, gateway device 329 a may include a non-transitorycomputer-readable medium (e.g., a memory, a programmable circuit board,a field programmable gate array, an integrated circuit, any combinationthereof, and/or the like) including (e.g., implementing, embodying,storing, and/or the like) the trained rhythm change classifier (whichmay include, e.g., at least one neural network trained based on thehistorical collection of a plurality of ECG signal portions with knownrhythm change information), as described herein. Additionally oralternatively, gateway device 329 a may include at least one processoroperatively connected to the non-transitory computer-readable medium.

In some embodiments, gateway device 329 a may enable communicationbetween heart monitoring device 202 a and remote computer system 204 a,as described herein.

As shown in FIG. 3A, at 316 a, heart monitoring device 302 a and/orgateway device 329 a (e.g., the processor(s) thereof) may be configuredto receive the ECG signal(s). In some embodiments, heart monitoringdevice 302 a may be configured to receive the ECG signal(s) via the ECGchannel(s), as described herein. Additionally or alternatively, heartmonitoring device 302 a may be configured to communicate (e.g.,transmit) the ECG signal(s) to gateway device 329 a, and/or gatewaydevice 329 a may receive the ECG signals from heart monitoring device302 a, as described herein.

As shown in FIG. 3A, at 318 a, heart monitoring device 302 a and/orgateway device 329 a (e.g., the processor(s) thereof) may be configuredto detect with the rhythm change classifier time data corresponding to apredetermined rhythm change in the at least one ECG signal, as describedherein. For example, the predetermined rhythm change may be associatedwith an arrhythmia (e.g., a change in heart rate, atrial fibrillation,flutter, supraventricular tachycardia, ventricular tachycardia, pause,AV block, ventricular fibrillation, bigeminy, trigeminy, ventricularectopic beats, bradycardia, tachycardia, a change in morphology of theat least one ECG signal, any combination thereof, and/or the like), asdescribed herein. Additionally or alternatively, the time data mayinclude at least one of a start time, a time interval, any combinationthereof, and/or the like, as described herein.

As shown in FIG. 3A, at 320 a, heart monitoring device 302 a and/orgateway device 329 a (e.g., the processor(s) thereof) may be configuredto determine (e.g., based on the detected time data) at least one ECGsignal portion associated with the detected time data corresponding tothe predetermined rhythm change in the ECG signal(s), as describedherein.

In some embodiments, the ECG channel(s) may include a plurality of ECGchannels, as described herein. Additionally or alternatively, the ECGsignal(s) may include at least one respective ECG signal associated witheach respective ECG channel, as described herein. In some embodiments,the plurality of ECG channels may include a first ECG channel and asecond ECG channel, as described herein. Additionally or alternatively,the ECG signal(s) may include a first respective ECG signal associatedwith the first ECG channel and a second respective ECG signal associatedwith the second ECG channel, as described herein. In some embodiments,the first respective ECG signal may be substantially orthogonal to thesecond respective ECG signal, as described herein.

In some embodiments, the neural network(s) of the rhythm changeclassifier (e.g., of heart monitoring device 302 a and/or gateway device329 a) may include a plurality of Siamese branches, as described herein.Additionally or alternatively, each respective Siamese branch may beassociated with a respective ECG channel (e.g., of the plurality of ECGchannels), as described herein.

In some embodiments, heart monitoring device 302 a and/or gateway device329 a (e.g., processor(s) thereof) may be further configured to detect(e.g., with the trained rhythm change classifier) the predeterminedrhythm change based on the at least one ECG signal. In some embodiments,heart monitoring device 302 a may further include at least one sensorand associated sensor circuitry configured to sense non-ECG biometricdata of the patient (which, in some embodiments, may be communicated togateway device 329 a), as described herein. Additionally oralternatively, detecting the predetermined rhythm change may be furtherbased on the non-ECG biometric data of the patient (e.g., non-ECGbiometric data of the patient may be input into the neural network(s) ofthe rhythm change classifier, may be combined with the output of therhythm change classifier, and/or the like), as described herein

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one baseline ECG signal portion of thepatient, as described herein.

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one calibration measurement of the patient, asdescribed herein.

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one reference vector of the patient, asdescribed herein.

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one previous ECG signal portion, as describedherein.

In some embodiments, the processor (e.g., of heart monitoring device 302a and/or gateway device 329 a) may further determine (e.g., with therhythm change classifier) a confidence score associated with thepredetermined rhythm change based on the at least one ECG signal, asdescribed herein.

As shown in FIG. 3A, at 322 a, heart monitoring device 302 a and/orgateway device 329 a (e.g., the processor(s) thereof) may communicate(e.g., transmit and/or the like) the determined ECG signal portion(s) toremote computer system 304 a, as described herein. Additionally oralternatively, heart monitoring device 302 a and/or gateway device 329 a(e.g., the processor(s) thereof) may be configured to communicate anindication (e.g., a flag, an indicator, a confidence score, a mark,metadata, the time data, and/or the like) associated with thepredetermined rhythm change detected (e.g., identified and/or the like)in the ECG signal portion(s).

In some embodiments, the processor (e.g., of heart monitoring device 202a and/or gateway device 329 a) may further communicate (e.g., transmitand/or the like) at least one second ECG signal portion of the ECGsignal(s) to remote computer system 304 a, as described herein.Additionally or alternatively, the second ECG signal portion(s) may beindependent of the detected time data corresponding to the predeterminedrhythm change in the ECG signal(s), as described herein.

In some embodiments, gateway device 329 a may enable communicationbetween heart monitoring device 302 a and remote computer system 304 a,as described herein. For example, transmitting the determined ECG signalportion(s) to remote computer system 204 a may include heart monitoringdevice 202 a communicating (e.g., transmitting and/or the like) thedetermined ECG signal portion(s) to gateway device 329 a, as describedherein.

In some embodiments, remote computing system 304 a may receive thedetermined ECG signal portion(s) (e.g., from heart monitoring device 302a and/or gateway device 329 a).

As shown in FIG. 3A, at 324 a, remote computing system 304 a may analyzethe determined ECG signal portion(s) to classify a type of arrhythmiafor the rhythm change(s) in the ECG signal(s), as described herein. Insome embodiments, the type of arrhythmia may include at least one of achange in heart rate, atrial fibrillation, flutter, supraventriculartachycardia, ventricular tachycardia, pause, AV block, ventricularfibrillation, bigeminy, trigeminy, ventricular ectopic beats,bradycardia, tachycardia, a change in morphology of the at least one ECGsignal, any combination thereof, and/or the like, as described herein.

In some embodiments, remote computer system 304 a may include anarrhythmia type classifier (e.g., including at least one (second) neuralnetwork trained based on a (second) historical collection of a (second)plurality of ECG signal portions with known arrhythmia typeinformation), as described herein. Additionally or alternatively,analyzing the at least one determined ECG signal portion may includeremote computer system 304 a detecting with the arrhythmia typeclassifier the type of arrhythmia associated with the rhythm changebased on the determined ECG signal portion(s), as described herein.

In some embodiments, remote computer system 304 a may analyze thedetermined ECG signal portion(s) to identify at least one arrhythmiaassociated with the rhythm change in the at least one ECG signal, asdescribed herein. In some embodiments, the arrhythmia may be one or morerare arrhythmias on which the remote computer system 304 a may havepreviously been trained. Additionally or alternatively, remote computersystem 304 a may use any suitable signal processing technique (e.g.,separate from or including the arrhythmia type classifier as describedherein) to identify the rare arrhythmia(s), as described herein.

In some embodiments, the determined ECG signal portion(s) may include aplurality of determined ECG signal portions. Additionally oralternatively, remote computer system 304 a may receive the plurality ofdetermined ECG signal portions from heart monitoring device 302 a and/orgateway device 329 a, as described herein. In some embodiments, remotecomputer system 304 a may analyze each respective determined ECG signalportion to classify a respective class for each respective determinedECG signal potion, as described herein. Additionally or alternatively,the class for at least two respective determined ECG signal potions mayinclude a first class (e.g., at least two ECG signal portions may belongto the same class/grouping), as described herein.

As shown in FIG. 3A, at 326 a, remote computer system 304 a maycommunicate (e.g., transmit and/or the like) at least one messageassociated with the determined ECG signal portion(s) and/or the type ofarrhythmia associated with the rhythm change, as described herein. Forexample, the message(s) may be communicated from remote computer system304 a to technician device 308 a, as described herein.

In some embodiments, remote computer system 304 a may transmit at leastone message associated with the second ECG signal portion(s) (e.g.,randomly determined second ECG signal portion(s), second ECG signalportion(s) determined to have a confidence score below a first thresholdand above a second threshold, and/or the like, as described herein) totechnician device 308 a, as described herein.

In some embodiments, remote computer system 304 a may transmit at leastone message associated with the at least two respective determined ECGsignal portions and the first class to technician device 308 a, asdescribed herein.

As shown in FIG. 3A, at 328 a, technician device 308 b may receive atleast one annotation associated with the ECG signal portion(s), e.g.,via input from a user (e.g., a technician and/or the like).

As shown in FIG. 3A, at 330 a, remote computer system 304 a may receiveannotation data associated with the annotation(s) from technician device308 a, as described herein. For example, the annotation data may becommunicated from technician device 308 a to remote computer system 304a, as described herein. Additionally or alternatively, the ECG signalportion(s) associated with such annotation(s) may be communicated withthe annotation data, as described herein.

In some embodiments, remote computer system 304 a may receive (e.g.,from technician device 308 a) annotation data associated withannotation(s) for the second ECG signal portion(s) (e.g., randomlydetermined second ECG signal portion(s), second ECG signal portion(s)determined to have a confidence score below a first threshold and abovea second threshold, and/or the like, as described herein), as describedherein.

As shown in FIG. 3A, at 332 a, remote computer system 304 a may retrainthe rhythm change classifier (and/or train an updated rhythm changeclassifier) based on the historical collection of the plurality of ECGsignal portions with the known rhythm change information, the second ECGsignal portion(s), and the annotation data associated therewith, asdescribed herein. In some embodiments, remote computer system 304 a mayretrain the arrhythmia type classifier based on the historicalcollection of the plurality of ECG signal portions with the known rhythmchange information, the second ECG signal portion(s), and the annotationdata associated therewith.

As shown in FIG. 3A, at 334 a, remote computer system 304 a maycommunicate (e.g., transmit, write, and/or the like) the annotation datato data repository 306 a, as described herein. Additionally oralternatively, the ECG signal portion(s) associated with suchannotation(s) may be communicated with the annotation data, as describedherein.

In some embodiments, annotation data and the ECG signal portion(s)associated with such annotation(s) may be added to the historicalcollection of the plurality of ECG signal portions. For example, theannotation data may be stored as the known rhythm change informationand/or the known arrhythmia type information for the ECG signalportion(s) associated with such annotation(s). In some embodiments,remote computer system 304 a may retrain the rhythm change classifier(and/or train an updated rhythm change classifier) based on thehistorical collection of the plurality of ECG signal portions with theknown rhythm change information (which may now include the ECG signalportion(s) and/or the annotation data associated therewith). In someembodiments, remote computer system 304 a may retrain the arrhythmiatype classifier based on the historical collection of the plurality ofECG signal portions with the known arrhythmia type information (whichmay now include the ECG signal portion(s) and/or the annotation dataassociated therewith).

In some embodiments, remote computer system 304 a may add annotationdata associated with at least one annotation for the second ECG signalportion(s) (e.g., randomly determined second ECG signal portion(s),second ECG signal portion(s) determined to have a confidence score belowa first threshold and above a second threshold, and/or the like, asdescribed herein) to the historical collection of the plurality of ECGsignal portions in data repository 306 a. In some embodiments, remotecomputer system 304 a may retrain the rhythm change classifier (and/ortrain an updated rhythm change classifier) based on the historicalcollection of the plurality of ECG signal portions with the known rhythmchange information (which may now include the second ECG signalportion(s) and/or the annotation data associated therewith). In someembodiments, remote computer system 304 a may retrain the arrhythmiatype classifier based on the historical collection of the plurality ofECG signal portions with the known arrhythmia type information (whichmay now include the second ECG signal portion(s) and/or the annotationdata associated therewith).

As shown in FIG. 3A, at 336 a, remote computer system 304 a maycommunicate the retrained rhythm change classifier (and/or trainedupdated rhythm change classifier) and/or weights thereof to heartmonitoring device 302 a and/or gateway device 329 a, as describedherein. Additionally or alternatively, a copy of the retrained rhythmchange classifier (and/or trained updated rhythm change classifier)and/or weights thereof may be downloaded from remote computer system 304a and/or installed on (e.g., uploaded to, written to, configured on,and/or the like) at least one non-transitory computer readable medium(e.g., e.g., memory, programmable circuit board, FPGA, integratedcircuit, any combination thereof, and/or the like), which may beinstalled in and/or part of heart monitoring device 302 a and/or gatewaydevice 329 a.

Referring now to FIG. 3B, FIG. 3B shows an example swim lane diagram ofa process 300 b for arrhythmia monitoring, according to someembodiments. In addition to system components, the process 300 b alsoshows communication flows between the system components. As shown inFIG. 3B, in some embodiments, heart monitoring device 302 b may be thesame as or similar to heart monitoring device 102, heart monitoringdevices 202 a and/or 202 b, heart monitoring device 302 a, and/or thelike. In some embodiments, remote computer system 304 b may be the sameas or similar to remote computer system 104, remote computer systems 204a, 204 b, and/or 204 c, remote computer system 304 a, and/or the like.In some embodiments, data repository 306 b may be the same as or similarto data repository 106, data repositories 206 a, 206 b, and/or 206 c,data repository 306 a, and/or the like. In some embodiments, techniciandevice 308 b may be the same as or similar to technician device 108,technician devices 208 a, 208 b, and/or 208 c, technician device 308 a,and/or the like. In some embodiments, gateway device 329 b may be thesame as or similar to gateway device 129, gateway devices 229 a and/or229 b, gateway device 329 a, and/or the like.

As shown in FIG. 3B, at 310 b, remote computer system 304 b may receive(e.g., retrieve, search for, send a request and/or query to cause datarepository 206 b to communicate, and/or the like) a historicalcollection of a plurality of ECG signal portions and information relatedthereto (e.g., known rhythm change information, known arrhythmia typeinformation, and/or the like), e.g., from data repository 306 b, asdescribed herein.

As shown in FIG. 3B, at 312 b, remote computer system 304 b may train atleast one neural network of at least one classifier (e.g., an arrhythmiatype classifier and/or the like) based on the historical collection of aplurality of ECG signal portions and information related thereto (e.g.,known arrhythmia type information and/or the like), as described herein.

In some embodiments, an arrhythmia type classifier may include at leastone neural network (e.g., at least one second neural network), asdescribed herein. Additionally or alternatively, the at least one(second) neural network may include at least one of a deep neuralnetwork, a convolutional neural network, a recurrent neural network, anattention network, a fully connected neural network, any combinationthereof, and/or the like, as described herein. In some embodiments, theneural network(s) may include a plurality of Siamese branches (e.g.,each respective Siamese branch associated with a respective ECGchannel), as described herein.

In some embodiments, remote computer system 304 b may train anarrhythmia type classifier by predicting, with the arrhythmia typeclassifier, a predicted type of arrhythmia in each respective ECG signalportion of the historical collection of the plurality of ECG signalportions (or a second plurality thereof), determining at least one errorvalue based on the predicted type of arrhythmia and the known arrhythmiatype information (e.g., a respective annotations associated with a knowntype of arrhythmia for each respective ECG signal portion), and trainingthe arrhythmia type classifier (e.g., updating the weights thereofand/or the like) based on the error value(s) (e.g., using backpropagation and/or the like). In some embodiments, the error value(s)may include one of a prediction error or a contrastive loss.

As shown in FIG. 3B, at 314 b, remote computer system 304 b maycommunicate a historical collection of a plurality of ECG signalportions and information related thereto (e.g., known rhythm changeinformation and/or the like) to heart monitoring device 302 b and/orgateway device 329 b, as described herein.

In some embodiments, gateway device 329 b may enable communicationbetween heart monitoring device 302 b and remote computer system 304 b,as described herein.

As shown in FIG. 3B, at 316 b, heart monitoring device 302 b and/orgateway device 329 b may train a rhythm change classifier, which may beimplemented by at least one non-transitory computer readable medium(e.g., e.g., a memory, a programmable circuit board, a fieldprogrammable gate array (FPGA), an integrated circuit, any combinationthereof, and/or the like) that may be installed in and/or part of heartmonitoring device 302 b and/or gateway device 329 b, as describedherein.

In some embodiments, heart monitoring device 302 b may be an externalheart monitoring device for a patient, as described herein. For example,(external) heart monitoring device 302 b may include a plurality of ECGelectrodes configured to sense surface ECG activity of the patient.Additionally or alternatively, (external) heart monitoring device 302 bmay include ECG processing circuitry configured to process the surfaceECG activity of the patient to provide at least one ECG signal for thepatient on at least one ECG channel.

In some embodiments, (external) heart monitoring device 302 b mayinclude a non-transitory computer-readable medium (e.g., a memory, aprogrammable circuit board, a field programmable gate array, anintegrated circuit, any combination thereof, and/or the like) including(e.g., implementing, embodying, storing, and/or the like) the rhythmchange classifier (which may include, e.g., at least one neuralnetwork), as described herein. Additionally or alternatively, (external)heart monitoring device 302 b may include at least one processoroperatively connected to the ECG channel(s) and the non-transitorycomputer-readable medium.

In some embodiments, gateway device 329 b may include a non-transitorycomputer-readable medium (e.g., a memory, a programmable circuit board,a field programmable gate array, an integrated circuit, any combinationthereof, and/or the like) including (e.g., implementing, embodying,storing, and/or the like) the trained rhythm change classifier (whichmay include, e.g., at least one neural network trained based on thehistorical collection of a plurality of ECG signal portions with knownrhythm change information), as described herein. Additionally oralternatively, gateway device 329 b may include at least one processoroperatively connected to the non-transitory computer-readable medium.

In some embodiments, a rhythm change classifier may include at least oneneural network, as described herein. Additionally or alternatively, theat least one neural network may include at least one of a convolutionalneural network, a recurrent neural network, an attention network, afully connected neural network, any combination thereof, and/or thelike, as described herein. In some embodiments, the neural network(s)may include a plurality of Siamese branches (e.g., each respectiveSiamese branch associated with a respective ECG channel), as describedherein.

In some embodiments, heart monitoring device 302 b and/or gateway device329 b may train the rhythm change classifier by predicting, with therhythm change classifier, predicted rhythm change information (e.g.,data (e.g., probability, confidence score, and/or the like) associatedwith a predicted rhythm change, data (e.g., probability, confidencescore, and/or the like) associated with a lack of a predicted rhythmchange, and/or the like) for each ECG signal portion of the historicalcollection of the plurality of ECG signal portions, determining at leastone error value based on the predicted rhythm change information and theknown rhythm change information, and training the rhythm changeclassifier (e.g., updating the weights thereof and/or the like) based onthe error value(s) (e.g., using back propagation and/or the like), asdescribed herein. In some embodiments, the error value(s) may includeone of a prediction error or a contrastive loss, as described herein.

In some embodiments, the historical collection of the plurality of ECGsignal portions may include a first ECG signal portion associated with afirst time and a second ECG signal portion associated with a second timeafter the first time, as described herein. Additionally oralternatively, heart monitoring device 302 b and/or gateway device 329 bmay train the rhythm change classifier by predicting, with the rhythmchange classifier, a predicted ECG signal portion associated with thesecond time based on the first ECG signal portion, determining at leastone error value based on the predicted ECG signal portion and the secondECG signal portion, and training the rhythm change classifier (e.g.,updating the weights thereof and/or the like) based on the errorvalue(s) (e.g., using back propagation and/or the like), as describedherein. In some embodiments, the error value(s) may include one of aprediction error or a contrastive loss, as described herein.

In some embodiments, the historical collection of the plurality of ECGsignal portions may include a first ECG signal portion associated with afirst time and a second ECG signal portion associated with a secondtime, as described herein. Additionally or alternatively, heartmonitoring device 302 b and/or gateway device 329 b may train the rhythmchange classifier by predicting, with the rhythm change classifier, apredicted time associated with the second ECG signal portion based onthe a first ECG signal portion and the second ECG signal, determining atleast one error value based on the predicted time and the second time,and training the rhythm change classifier (e.g., updating the weightsthereof and/or the like) based on the error value(s) (e.g., using backpropagation and/or the like), as described herein. In some embodiments,the error value(s) may include one of a prediction error or acontrastive loss, as described herein.

In some embodiments, there may be an insufficient number of ECG signalportions in the historical collection of the plurality of ECG signalportions with known rhythm change information to train the rhythm changeclassifier to perform the desired task (e.g., detect and/or identify atleast one predetermined rhythm changes), as described herein.Additionally or alternatively, there may be sufficient data (e.g.,historical ECG signal portions and/or the like) to train the rhythmchange classifier to perform a separate task (e.g., which may be relatedin some way to the target task), as described herein. In someembodiments, the rhythm change classifier may be trained to perform theseparate task (e.g., counting R-peaks, determining heart rate, and/orthe like based on the ECG signal(s)), as described herein. Additionallyor alternatively, the rhythm change classifier may then be adapted toperform the target task, as described herein. For example, in someembodiments, the rhythm change classifier may be retrained using thelimited amount of ECG signal portions in the historical collection ofthe plurality of ECG signal portions with known rhythm changeinformation and/or the like, as described herein. Additionally oralternatively, the rhythm change classifier may be used to perform theseparate task (e.g., counting R-peaks, determining heart rate, and/orthe like based on the ECG signal(s)), and the output thereof may beapplied to the target task, as described herein. For example, theprocessor (e.g., of heart monitoring device 302 b and/or gateway device329 b) may detect, with the rhythm change classifier, at least one of acount of peaks or a heart rate based on the at least one ECG signal, asdescribed herein. Additionally or alternatively, the processor (e.g., ofheart monitoring device 302 b and/or gateway device 329 b) may determinethe detected at least one of the count of peaks or the heart rate isabove a first threshold (e.g., tachycardia onset threshold) for thepatient or below a second threshold (e.g., bradycardia onset threshold)for the patient (e.g., wherein the second threshold for the patient maybe less than the first threshold for the patient), as described herein.

In some embodiments, there may be an insufficient number of ECG signalportions associated with (e.g., sensed from and/or the like) theplurality of ECG electrodes of the heart monitoring device 302 b in thehistorical collection of the plurality of ECG signal portions with knownrhythm change information to train the rhythm change classifier for theECG signal(s) received form the plurality of ECG electrodes of the heartmonitoring device 302 a, as described herein. Additionally oralternatively, there may be sufficient data (e.g., historical ECG signalportions and/or the like) associated with (e.g., sensed from and/or thelike) a second plurality of ECG electrodes (e.g., electrodes from an ECGdevice separate from the heart monitoring device 302 b, such as a12-lead ECG sensor, a separate external and/or wearable heart monitoringdevice, and/or the like) independent of the plurality of ECG electrodesof the heart monitoring device 302 a to train the rhythm changeclassifier based on the second plurality of ECG electrodes, as describedherein. In some embodiments, the rhythm change classifier may be trainedbased on the ECG signal portion(s) associated with (e.g., sensed fromand/or the like) the second plurality of ECG electrodes, as describedherein. Additionally or alternatively, the rhythm change classifier maythen be adapted to detect the predetermined rhythm change(s) based onthe plurality of ECG electrodes of heart monitoring device 202 b, asdescribed herein. In some embodiments, heart monitoring device 202 band/or gateway device 329 b may determine (e.g., calculate and/or thelike) a transform (e.g., vector projection and/or the like) of the ECGsignal portion(s) associated with the second plurality of ECG electrodesto the plurality of ECG electrodes of heart monitoring device 302 b, andthe transform of the ECG signal portion(s) may be used to train therhythm change classifier as if the ECG signal portions were associatedwith (e.g., sensed from and/or the like) the plurality of ECG electrodesof the heart monitoring device 302 b, as described herein.

As shown in FIG. 3B, at 318 b, heart monitoring device 302 b and/orgateway device 329 b (e.g., the processor(s) thereof) may be configuredto receive the ECG signal(s). In some embodiments, heart monitoringdevice 302 a may be configured to receive the ECG signal(s) via the ECGchannel(s), as described herein. Additionally or alternatively, heartmonitoring device 302 a may be configured to communicate (e.g.,transmit) the ECG signal(s) to gateway device 329 a, and/or gatewaydevice 329 a may receive the ECG signals from heart monitoring device302 a, as described herein.

As shown in FIG. 3B, at 320 b, heart monitoring device 302 b and/orgateway device 329 b (e.g., the processor(s) thereof) may be configuredto detect with the rhythm change classifier time data corresponding to apredetermined rhythm change in ECG signal(s), as described herein.

In some embodiments, the neural network(s) of the rhythm changeclassifier (e.g., of heart monitoring device 302 b and/or gateway device329 b) may include a plurality of Siamese branches, as described herein.

In some embodiments, heart monitoring device 302 b and/or gateway device329 b (e.g., processor(s) thereof) may be further configured to detect(e.g., with the trained rhythm change classifier) the predeterminedrhythm change based on the at least one ECG signal, as described herein.In some embodiments, heart monitoring device 302 b may further includeat least one sensor and associated sensor circuitry configured to sensenon-ECG biometric data of the patient (which, in some embodiments, maybe communicated to gateway device 329 b), as described herein.Additionally or alternatively, detecting the predetermined rhythm changemay be further based on the non-ECG biometric data of the patient (e.g.,non-ECG biometric data of the patient may be input into the neuralnetwork(s) of the rhythm change classifier, may be combined with theoutput of the rhythm change classifier, and/or the like), as describedherein.

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one baseline ECG signal portion of thepatient, as described herein.

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one calibration measurement of the patient, asdescribed herein.

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one reference vector of the patient, asdescribed herein.

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one previous ECG signal portion, as describedherein.

In some embodiments, the processor (e.g., of heart monitoring device 302b and/or gateway device 329 b) may further determine (e.g., with therhythm change classifier) a confidence score associated with thepredetermined rhythm change based on the at least one ECG signal, asdescribed herein.

As shown in FIG. 3B, at 322 b, heart monitoring device 302 a and/orgateway device 329 b (e.g., the processor(s) thereof) may be configuredto determine based on the detected time data at least one ECG signalportion associated with the detected time data corresponding to thepredetermined rhythm change in the ECG signal(s), as described herein.

As shown in FIG. 3B, at 324 b, heart monitoring device 302 b and/orgateway device 329 b (e.g., the processor(s) thereof) may communicate(e.g., transmit and/or the like) the determined ECG signal portion(s) toremote computer system 304 b, as described herein. Additionally oralternatively, heart monitoring device 302 b and/or gateway device 329 b(e.g., the processor(s) thereof) may be configured to communicate anindication (e.g., a flag, an indicator, a confidence score, a mark,metadata, the time data, and/or the like) associated with thepredetermined rhythm change detected (e.g., identified and/or the like)in the ECG signal portion(s), as described herein.

In some embodiments, the processor (e.g., of heart monitoring device 302b and/or gateway device 329 b) may further communicate (e.g., transmitand/or the like) at least one second ECG signal portion of the ECGsignal(s) to remote computer system 304 b, as described herein.Additionally or alternatively, the second ECG signal portion(s) may beindependent of the detected time data corresponding to the predeterminedrhythm change in the ECG signal(s), as described herein.

In some embodiments, gateway device 329 b may enable communicationbetween heart monitoring device 302 b and remote computer system 304 b,as described herein.

In some embodiments, remote computing system 304 b may receive thedetermined ECG signal portion(s) (e.g., from heart monitoring device 302b and/or gateway device 329 b), as described herein.

As shown in FIG. 3B, at 326 b, remote computing system 304 b may analyzethe determined ECG signal portion(s) to classify a type of arrhythmiafor the rhythm change(s) in the ECG signal(s), as described herein. Forexample, remote computer system 304 b may include an arrhythmia typeclassifier (e.g., including at least one (second) neural network trainedbased on a (second) historical collection of a (second) plurality of ECGsignal portions with known arrhythmia type information), as describedherein.

In some embodiments, remote computer system 304 b may analyze thedetermined ECG signal portion(s) to identify at least one arrhythmiaassociated with the rhythm change in the at least one ECG signal, asdescribed herein. In some embodiments, the arrhythmia may be one or morerare arrhythmias on which the remote computer system 204 a may havepreviously been trained. Additionally or alternatively, remote computersystem 204 a may use any suitable signal processing technique (e.g.,separate from or including the arrhythmia type classifier as describedherein) to identify the rare arrhythmia(s), as described herein.

In some embodiments, the determined ECG signal portion(s) may include aplurality of determined ECG signal portions, as described herein.Additionally or alternatively, remote computer system 304 b may receivethe plurality of determined ECG signal portions from heart monitoringdevice 302 b and/or gateway device 329 b, as described herein. In someembodiments, remote computer system 304 b may analyze each respectivedetermined ECG signal portion to classify a respective class for eachrespective determined ECG signal potion, as described herein.

As shown in FIG. 3B, at 328 b, remote computer system 304 b maycommunicate (e.g., transmit and/or the like) at least one messageassociated with the determined ECG signal portion(s) and/or the type ofarrhythmia associated with the rhythm change, as described herein. Forexample, the message(s) may be communicated from remote computer system304 b to technician device 308 b, as described herein.

In some embodiments, remote computer system 304 b may transmit at leastone message associated with the second ECG signal portion(s) (e.g.,randomly determined second ECG signal portion(s), second ECG signalportion(s) determined to have a confidence score below a first thresholdand above a second threshold, and/or the like, as described herein) totechnician device 308 b, as described herein.

In some embodiments, remote computer system 304 b may transmit at leastone message associated with the at least two respective determined ECGsignal portions and the first class to technician device 308 b, asdescribed herein.

As shown in FIG. 3B, at 330 b, technician device 308 b may receive atleast one annotation associated with the ECG signal portion(s), e.g.,via input from a user (e.g., a technician and/or the like).

As shown in FIG. 3B, at 332 b, remote computer system 304 b may receiveannotation data associated with at least one annotation from techniciandevice 308 a, as described herein. For example, the annotation data maybe communicated from technician device 308 b to remote computer system304 b, as described herein. Additionally or alternatively, the ECGsignal portion(s) associated with such annotation(s) may be communicatedwith the annotation data, as described herein.

In some embodiments, remote computer system 304 b may receive (e.g.,from technician device 308 b) annotation data associated with at leastone annotation for the second ECG signal portion(s) (e.g., randomlydetermined second ECG signal portion(s), second ECG signal portion(s)determined to have a confidence score below a first threshold and abovea second threshold, and/or the like, as described herein).

As shown in FIG. 3B, at 334 b, remote computer system 304 a may retrainthe arrhythmia type classifier based on the historical collection of theplurality of ECG signal portions with the known rhythm changeinformation, the second ECG signal portion(s), and the annotation dataassociated therewith, as described herein.

As shown in FIG. 3B, at 336 b, remote computer system 304 b maycommunicate (e.g., transmit, write, and/or the like) the annotation datato data repository 306 b, as described herein. Additionally oralternatively, the ECG signal portion(s) associated with suchannotation(s) may be communicated with the annotation data, as describedherein.

In some embodiments, annotation data and the ECG signal portion(s)associated with such annotation(s) may be added to the historicalcollection of the plurality of ECG signal portions, as described herein.In some embodiments, remote computer system 304 b may retrain thearrhythmia type classifier based on the historical collection of theplurality of ECG signal portions with the known arrhythmia typeinformation (which may now include the ECG signal portion(s) and/or theannotation data associated therewith).

In some embodiments, remote computer system 304 b may add annotationdata associated with at least one annotation for the second ECG signalportion(s) (e.g., randomly determined second ECG signal portion(s),second ECG signal portion(s) determined to have a confidence score belowa first threshold and above a second threshold, and/or the like, asdescribed herein) to the historical collection of the plurality of ECGsignal portions in data repository 306 b, as described herein. In someembodiments, remote computer system 304 b may retrain the arrhythmiatype classifier based on the historical collection of the plurality ofECG signal portions with the known arrhythmia type information (whichmay now include the second ECG signal portion(s) and/or the annotationdata associated therewith), as described herein.

As shown in FIG. 3B, at 338 b, remote computer system 304 b maycommunicate (e.g., transmit and/or the like) the annotation dataassociated with at least one annotation for the second ECG signalportion(s) to heart monitoring device 302 b and/or gateway device 329 b,as described herein. Additionally or alternatively, heart monitoringdevice 302 b and/or gateway device 329 b (e.g., processor(s) thereof)may retrain the rhythm change classifier (and/or train an updated rhythmchange classifier) based on the historical collection of the pluralityof ECG signal portions with the known rhythm change information, thesecond ECG signal portion(s), and the annotation data associatedtherewith, as described herein.

Referring now to FIG. 3C, FIG. 3C shows an example swim lane diagram ofa process 300 c for arrhythmia monitoring, according to someembodiments. In addition to system components, the process 300 c alsoshows communication flows between the system components. As shown inFIG. 3C, in some embodiments, remote computer system 304 c may be thesame as or similar to remote computer system 104, remote computersystems 204 a, 204 b, and/or 204 c, remote computer systems 304 a and/or304 b, and/or the like. In some embodiments, data repository 306 c maybe the same as or similar to data repository 106, data repositories 206a, 206 b, and/or 206 c, data repositories 306 a and/or 306 b, and/or thelike. In some embodiments, technician device 308 c may be the same as orsimilar to technician device 108, technician devices 208 a, 208 b,and/or 208 c, technician devices 308 a and/or 308 b, and/or the like. Insome embodiments, supervisor device 310 c may be the same as or similarto supervisor device 110, supervisor device 210 c, and/or the like.

As shown in FIG. 3C, at 312 c, remote computer system 304 c may receive(e.g., retrieve, search for, send a request and/or query to cause datarepository 206 c to communicate, and/or the like) a historicalcollection of a plurality of ECG signal portions and information relatedthereto (e.g., known arrhythmia type information and/or the like), e.g.,from data repository 306 c, as described herein.

As shown in FIG. 3C, at 314 c, remote computer system 304 c may train atleast one neural network of at least one classifier (e.g., an arrhythmiatype classifier and/or the like) based on the historical collection of aplurality of ECG signal portions and information related thereto (e.g.,known arrhythmia type information and/or the like), as described herein.

In some embodiments, an arrhythmia type classifier may include at leastone neural network (e.g., at least one second neural network), asdescribed herein. Additionally or alternatively, the at least one(second) neural network may include at least one of a deep neuralnetwork, a convolutional neural network, a recurrent neural network, anattention network, a fully connected neural network, any combinationthereof, and/or the like, as described herein. In some embodiments, theneural network(s) may include a plurality of Siamese branches (e.g.,each respective Siamese branch associated with a respective ECGchannel), as described herein.

In some embodiments, remote computer system 304 c may train anarrhythmia type classifier by predicting, with the arrhythmia typeclassifier, a predicted type of arrhythmia in each respective ECG signalportion of the historical collection of the plurality of ECG signalportions (or a second plurality thereof), determining at least one errorvalue based on the predicted type of arrhythmia and the known arrhythmiatype information (e.g., a respective annotations associated with a knowntype of arrhythmia for each respective ECG signal portion), and trainingthe arrhythmia type classifier (e.g., updating the weights thereofand/or the like) based on the error value(s) (e.g., using backpropagation and/or the like), as described herein. In some embodiments,the error value(s) may include one of a prediction error or acontrastive loss, as described herein.

In some embodiments, the known arrhythmia type information may include aplurality of annotations, as described herein. For example, eachannotation may be associated with a respective ECG signal portion of theplurality of ECG signal portions, as described herein. In someembodiments, remote computer system 304 c may train the arrhythmia typeclassifier based on the plurality of ECG signals and the plurality ofannotations, as described herein.

In some embodiments, the plurality of annotations may be from aplurality of technicians (e.g., a plurality of technician devices 308 cand/or the like), as described herein. Additionally or alternatively,each annotation annotations may be associated with a respectivetechnician of the plurality of technicians and/or a respective ECGsignal portion of the plurality of ECG signal portions, as describedherein. In some embodiments, the arrhythmia type classifier may betrained separately for each technician, as described herein. Forexample, for a first technician of the plurality of technicians, thearrhythmia type classifier may be trained (e.g., by remote computersystem 304 c) based on a subset of the plurality of ECG signals and theplurality of annotations associated with at least one other technicianof the plurality of technicians different than the first technician, asdescribed herein.

In some embodiments, each annotation may be associated with one possibletype of arrhythmia (e.g., a label associated with a possible type ofarrhythmia, a text string identifying at least one possible typearrhythmia, and/or the like) of the respective ECG signal(s) and/orportion(s) thereof, as described herein.

In some embodiments, there may be an insufficient number of ECG signalportions associated with (e.g., sensed from and/or the like) at leastone second ECG electrode in the historical collection of the pluralityof ECG signal portions with known arrhythmia type information (e.g.,annotations, labels, and/or the like) to train the arrhythmia typeclassifier for the ECG signal(s) received from the second ECGelectrode(s), as described herein. Additionally or alternatively, theremay be sufficient data (e.g., historical ECG signal portions and/or thelike) associated with (e.g., sensed from and/or the like) at least onefirst ECG electrode (e.g., electrodes from an ECG device separate fromthe second ECG electrode(s), such as a 12-lead ECG sensor, a separateexternal and/or wearable heart monitoring device, and/or the like)independent of the second ECG electrode(s) to train the rhythm changeclassifier based on the second ECG electrode(s), as described herein. Insome embodiments, the known arrhythmia type information may include aplurality of annotations, each of which may be associated with arespective ECG signal portion of a first plurality of ECG signalportions associated with the first ECG electrode(s), as describedherein. In some embodiments, each respective ECG signal portion of asecond plurality of ECG signal portions associated with the second ECGelectrode(s) may correspond to a respective ECG signal portion of thefirst plurality of ECG signal portions, as described herein. In someembodiments, remote computer system 304 c may train the arrhythmia typeclassifier by predicting, with the arrhythmia type classifier, apredicted type of arrhythmia in each respective ECG signal portion ofthe second plurality of ECG signal portions, determining at least oneerror value based on the predicted type of arrhythmia and the respectiveannotation of the plurality of annotations associated with a respectiveECG signal portion of the first plurality of ECG signal portionscorresponding to the respective ECG signal portion of the secondplurality of ECG signal portions, and training (e.g., updating theweights of and/or the like) the arrhythmia type classifier based on theat least one error value (e.g., based on back propagation and/or thelike), as described herein.

In some embodiments, ECG signal portions associated with multipleelectrodes may be combined (e.g., by vector addition, vector projection,a transform, and/or the like) to form extrapolated ECG signal portionsthat may be more familiar and/or suitable for review by a human user(e.g., technician and/or the like), as described herein. For example,the historical collection of the plurality of ECG signal portions mayinclude a first plurality of ECG signal portions of at least one firstECG signal based on first surface ECG activity sensed by at least onefirst ECG electrode and a second plurality of ECG signal portions of atleast one second ECG signal based on second surface ECG activity sensedby at least one second ECG electrode, as described herein. In someembodiments, each ECG signal portion of the first plurality of ECGsignal portions may be combined (e.g., by vector addition, vectorprojection, a transform, and/or the like) with a respective ECG signalportion of the second plurality of ECG signal portions to form aplurality of extrapolated ECG signal portions (e.g., by remote computersystem 304 c), as described herein. In some embodiments, the knownarrhythmia type information may include a plurality of annotations, asdescribed herein. Additionally or alternatively, each respectiveannotation may be associated with a respective extrapolated ECG signalportion of the plurality of extrapolated ECG signal portions, asdescribed herein.

In some embodiments, at least some of the plurality of ECG signalportions of the historical collection may be time warped (e.g., timedilated and/or the like) to form a plurality of warped ECG signalportions (e.g., by remote computer system 304 c using any suitablesignal processing technique for time warping, time dilation, and/or thelike), as described herein.

In some embodiments, at least some of the plurality of ECG signalportions of the historical collection may be at least one of filtered,inverted, any combination thereof, and/or the like (e.g., by remotecomputer system 304 c), as described herein.

In some embodiments, at least one noise signal portion may be combinedwith at least some of the plurality of ECG signal portions of thehistorical collection (e.g., by remote computer system 304 c), asdescribed herein.

In some embodiments, at least some of the plurality of ECG signalportions of the historical collection may be style transferred (e.g., byremote computer system 304 c), as described herein.

As shown in FIG. 3C, at 316 c, remote computer system 304 c may receiveat least one ECG signal and annotation data associated with at least oneannotation for each ECG signal, as described herein.

As shown in FIG. 3C, at 318 c, remote computer system 304 c may detect,with the arrhythmia type classifier, a type of arrhythmia in the ECGsignal(s) and time data associated with the detected type of arrhythmia,as described herein. For example, the time data may include at least oneof a start time, a time interval, any combination thereof, and/or thelike, as described herein. In some embodiments, remote computer system304 c may determine, based on the time data, at least one ECG signalportion associated with the detected type of arrhythmia in the ECGsignal(s), as described herein.

In some embodiments, remote computer system 304 c may determine aplausibility score for the annotation(s) based on the detected type ofarrhythmia. For example, an output of at least one neural network of thearrhythmia type classifier may include a confidence score (e.g., aprobability and/or the like) associated with each possible type ofarrhythmia, as described herein. For example, the type of arrhythmiadetermined by the arrhythmia type classifier may be the type ofarrhythmia with a highest confidence score (e.g., probability and/or thelike). Additionally or alternatively, each annotation may be associatedwith one possible type of arrhythmia (e.g., a label associated with apossible type of arrhythmia, a text string identifying at least onepossible type arrhythmia, and/or the like). In some embodiments,plausibility score of each annotation may be the confidence score (e.g.,determined by the arrhythmia type classifier) of the possible type ofarrhythmia associated with such annotation. In some embodiments, thearrhythmia type classifier may include a plurality of neural networks,and each such neural network may output a confidence score associatedwith at least one possible type of arrhythmia.

As shown in FIG. 3C, at 320 c, remote computer system 304 c may generateat least one message based on the at least one determined ECG signalportion and the plausibility score for the at least one annotation, asdescribed herein. For example, the message(s) may indicate at least oneof a recommendation to annotate the at least one determined ECG signalportion based on the detected type of arrhythmia, a recommendation toreevaluate the annotation data associated with the at least onedetermined ECG signal portion based on the plausibility score, and/orthe like.

In some embodiments, remote computer system 304 c may to determine theplausibility score is below a threshold. Additionally or alternatively,generating the message(s) may include remote computer system 304 cgenerating, based on the determination that the plausibility score isbelow the threshold, the at least one message indicating therecommendation to reevaluate the annotation data associated with the atleast one determined ECG signal portion, as described herein.

As shown in FIG. 3C, at 322 c, remote computer system 304 c may transmitthe at least some of the message(s) associated with the at least onedetermined ECG signal portion to technician device 308 c, as describedherein.

As shown in FIG. 3C, at 324 c, remote computer system 204 c may transmitthe at least some of the message(s) associated with the at least onedetermined ECG signal portion to supervisor device 310 c, as describedherein.

Referring now to FIG. 4A, FIG. 4A shows an example flow chart of aprocess 400 a for arrhythmia monitoring, according to some embodiments.In some embodiments, one or more of the steps of process 400 a may beperformed (e.g., completely, partially, and/or the like) by heartmonitoring device 102. In some non-limiting embodiments, one or more ofthe steps of process 400 a may be performed (e.g., completely,partially, and/or the like) by another system, another device, anothergroup of systems, or another group of devices, separate from orincluding heart monitoring device 102, such as remote computer system104, data repository 106, technician device 108, gateway device 129,and/or the like.

As shown in FIG. 4A, at step 402 a, a rhythm change classifier may bereceived and/or installed. For example, heart monitoring device 102and/or gateway device 129 may receive (e.g., from remote computer system104, data repository 106, and/or the like) and/or have installed thereon(e.g., in a non-transitory computer readable medium of heart monitoringdevice 102 and/or gateway device 129) a rhythm change classifier (and/ora plurality of weights thereof), as described herein. For example, therhythm change classifier may include at least one neural network trainedbased on a historical collection of a plurality of ECG signal portionswith known rhythm change information, as described herein.

In some embodiments, remote computer system 104 may train the rhythmchange classifier, as described herein. Additionally or alternatively,remote computer system 104 may communicate the trained rhythm changeclassifier to heart monitoring device 102 and/or gateway device 129, asdescribed herein.

In some embodiments, gateway device 129 may enable communication betweenheart monitoring device 102 and remote computer system 104, as describedherein.

As shown in FIG. 4A, at step 404 a, at least one ECG signal may bereceived. For example, heart monitoring device 102 and/or gateway device129 (e.g., the processor(s) thereof) may receive at least one ECGsignal. In some embodiments, heart monitoring device 102 may beconfigured to receive the ECG signal(s) via at least one ECG channel, asdescribed herein. Additionally or alternatively, heart monitoring device102 may be configured to communicate (e.g., transmit) the ECG signal(s)to gateway device 129, and/or gateway device 129 may receive the ECGsignals from heart monitoring device 102, as described herein.

As shown in FIG. 4A, at step 406 a, at least one of predetermined rhythmchange and/or a time thereof may be detected. For example, heartmonitoring device 102 and/or gateway device 129 (e.g., the processor(s)thereof) may detect, with the rhythm change classifier, time datacorresponding to a predetermined rhythm change in the at least one ECGsignal, as described herein. Additionally or alternatively, heartmonitoring device 102 and/or gateway device 129 (e.g., the processor(s)thereof) may detect, with the rhythm change classifier, thepredetermined rhythm change based on the at least one ECG signal, asdescribed herein.

In some embodiments, heart monitoring device 102 and/or gateway device129 (e.g., processor(s) thereof) may detect (e.g., with the trainedrhythm change classifier) the predetermined rhythm change based on theat least one ECG signal. In some embodiments, heart monitoring device102 may further include at least one sensor and associated sensorcircuitry configured to sense non-ECG biometric data of the patient(which, in some embodiments, may be communicated to gateway device 129),as described herein. Additionally or alternatively, detecting thepredetermined rhythm change may be further based on the non-ECGbiometric data of the patient (e.g., non-ECG biometric data of thepatient may be input into the neural network(s) of the rhythm changeclassifier, may be combined with the output of the rhythm changeclassifier, and/or the like), as described herein

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one baseline ECG signal portion of thepatient, as described herein.

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one calibration measurement of the patient, asdescribed herein.

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one reference vector of the patient, asdescribed herein.

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one previous ECG signal portion, as describedherein.

In some embodiments, the heart monitoring device 102 and/or gatewaydevice 129 (e.g., processor(s) thereof) may further determine (e.g.,with the rhythm change classifier) a confidence score associated withthe predetermined rhythm change based on the at least one ECG signal, asdescribed herein.

As shown in FIG. 4A, at step 408 a, at least one ECG signal portion maybe determined (e.g., based on the detected time data, the detectedpredetermined rhythm change, and/or the like. For example, heartmonitoring device 102 and/or gateway device 129 (e.g., the processor(s)thereof) may determine (e.g., based on the detected time data) at leastone ECG signal portion associated with the detected time datacorresponding to the predetermined rhythm change in the ECG signal(s),as described herein.

As shown in FIG. 4A, at step 410 a, the ECG signal portion(s) may betransmitted. For example, heart monitoring device 102 and/or gatewaydevice 129 may communicate (e.g., transmit and/or the like) thedetermined ECG signal portion(s) to remote computer system 104, asdescribed herein. Additionally or alternatively, heart monitoring device102 and/or gateway device 129 (e.g., the processor(s) thereof) maycommunicate an indication (e.g., a flag, an indicator, a confidencescore, a mark, metadata, the time data, and/or the like) associated withthe predetermined rhythm change detected (e.g., identified and/or thelike) in the ECG signal portion(s).

In some embodiments, heart monitoring device 102 and/or gateway device129 (e.g., the processor(s) thereof) may further communicate (e.g.,transmit and/or the like) at least one second ECG signal portion of theECG signal(s) to remote computer system 104, as described herein.Additionally or alternatively, the second ECG signal portion(s) may beindependent of the detected time data corresponding to the predeterminedrhythm change in the ECG signal(s), as described herein.

In some embodiments, gateway device 129 may enable communication betweenheart monitoring device 102 and remote computer system 104, as describedherein.

In some embodiments, remote computing system 104 may receive thedetermined ECG signal portion(s) (e.g., from heart monitoring device 102and/or gateway device 129).

As shown in FIG. 4A, at step 412 a, a retrained (e.g., updated and/orthe like) rhythm change classifier may be received. For example, heartmonitoring device 102 and/or gateway device 129 may receive theretrained (e.g., updated and/or the like) rhythm change classifier fromremote computer system 104.

In some embodiments, remote computing system 104 may analyze thedetermined ECG signal portion(s) to classify a type of arrhythmia forthe rhythm change(s) in the ECG signal(s), as described herein. Forexample, remote computer system 204 a may include an arrhythmia typeclassifier (e.g., including at least one (second) neural network trainedbased on a (second) historical collection of a (second) plurality of ECGsignal portions with known arrhythmia type information), as describedherein.

In some embodiments, remote computer system 104 may communicate (e.g.,transmit and/or the like) at least one message associated with thedetermined ECG signal portion(s) and/or the type of arrhythmiaassociated with the rhythm change, as described herein. For example, themessage(s) may be communicated from remote computer system 104 totechnician device 108, as described herein.

In some embodiments, remote computer system 104 may transmit at leastone message associated with the second ECG signal portion(s) (e.g.,randomly determined second ECG signal portion(s), second ECG signalportion(s) determined to have a confidence score below a first thresholdand above a second threshold, and/or the like, as described herein) totechnician device 108, as described herein.

In some embodiments, technician device 108 may receive at least oneannotation associated with the ECG signal portion(s), e.g., via inputfrom a user (e.g., a technician and/or the like). Additionally oralternatively, remote computer system 104 may receive annotation dataassociated with the annotation(s) from technician device 108, asdescribed herein.

In some embodiments, remote computer system 104 may retrain the rhythmchange classifier (and/or train an updated rhythm change classifier)based on the historical collection of the plurality of ECG signalportions with the known rhythm change information, the second ECG signalportion(s), and the annotation data associated therewith, as describedherein (e.g., before and/or after adding the second ECG signalportion(s) and the annotation data associated therewith to thehistorical collection, as described herein).

In some embodiments, remote computer system 104 may communicate theretrained rhythm change classifier (and/or trained updated rhythm changeclassifier) to heart monitoring device 102 and/or gateway device 129, asdescribed herein. Additionally or alternatively, a copy of the retrainedrhythm change classifier (and/or trained updated rhythm changeclassifier) may be downloaded from remote computer system 104 and/orinstalled on (e.g., uploaded to, written to, configured on, and/or thelike) at least one non-transitory computer readable medium (e.g., e.g.,memory, programmable circuit board, FPGA, integrated circuit, anycombination thereof, and/or the like), which may be installed in and/orpart of heart monitoring device 102 and/or gateway device 129.

In some non-limiting embodiments, process 400 a may include repeating atleast some steps (e.g., steps 404 a-410 a, 404 a-412 a, and/or thelike). For example, at least some such steps may be repeatedcontinuously, periodically, and/or the like. For example, ECG signal(s)may be received (404 a) continuously. Additionally or alternatively,received ECG signals may be analyzed using the rhythm change classifiercontinuously. For example, predetermined rhythm changes and/or time dataassociated therewith may be detected (406 a) as often as rhythm changesoccur in the ECG signal(s). Additionally or alternatively, the ECGsignal portion(s) may be determined (408 a) and/or transmitted (410 a)as often as rhythm changes and/or time data associated therewith may bedetected. For example, the rhythm change classifier may be retrained(412 a) periodically, continuously, and/or the like.

Referring now to FIG. 4B, FIG. 4B shows an example flow chart of aprocess 400 b for arrhythmia monitoring, according to some embodiments.In some embodiments, one or more of the steps of process 400 b may beperformed (e.g., completely, partially, and/or the like) by heartmonitoring device 102. In some non-limiting embodiments, one or more ofthe steps of process 400 b may be performed (e.g., completely,partially, and/or the like) by another system, another device, anothergroup of systems, or another group of devices, separate from orincluding heart monitoring device 102, such as remote computer system104, data repository 106, technician device 108, gateway device 129,and/or the like.

As shown in FIG. 4B, at step 402 b, a historical collection of aplurality of ECG signal portions and information related thereto (e.g.,known rhythm change information and/or the like) may be received. Forexample, heart monitoring device 102 and/or gateway device 129 mayreceive the historical collection of a plurality of ECG signal portionsand/or information related thereto from remote computer system 104, datarepository 106, and/or the like, as described herein.

As shown in FIG. 4B, at step 404 b, remote computer system a rhythmchange classifier may be trained. For example, heart monitoring device102 and/or gateway device 129 may train the rhythm change classifier,which may be implemented by at least one non-transitory computerreadable medium (e.g., e.g., a memory, a programmable circuit board, afield programmable gate array (FPGA), an integrated circuit, anycombination thereof, and/or the like) that may be installed in and/orpart of heart monitoring device 102 and/or gateway device 129, asdescribed herein.

In some embodiments, heart monitoring device 102 may be an externalheart monitoring device for a patient, as described herein.

In some embodiments, a rhythm change classifier may include at least oneneural network, as described herein. Additionally or alternatively, theat least one neural network may include at least one of a convolutionalneural network, a recurrent neural network, an attention network, afully connected neural network, any combination thereof, and/or thelike, as described herein. In some embodiments, the neural network(s)may include a plurality of Siamese branches (e.g., each respectiveSiamese branch associated with a respective ECG channel), as describedherein.

In some embodiments, heart monitoring device 102 and/or gateway device129 may train the rhythm change classifier by generating, with therhythm change classifier, predicted rhythm change information (e.g.,data (e.g., probability, confidence score, and/or the like) associatedwith a predicted rhythm change, data (e.g., probability, confidencescore, and/or the like) associated with a lack of a predicted rhythmchange, and/or the like) for each ECG signal portion of the historicalcollection of the plurality of ECG signal portions, determining at leastone error value based on the predicted rhythm change information and theknown rhythm change information, and updating the rhythm changeclassifier (e.g., updating the weights thereof and/or the like) based onthe error value(s) (e.g., using back propagation and/or the like), asdescribed herein. In some embodiments, the error value(s) may includeone of a prediction error or a contrastive loss, as described herein.

In some embodiments, the historical collection of the plurality of ECGsignal portions may include a first ECG signal portion associated with afirst time and a second ECG signal portion associated with a second timeafter the first time, as described herein. Additionally oralternatively, heart monitoring device 102 and/or gateway device 129 maytrain the rhythm change classifier by predicting, with the rhythm changeclassifier, a predicted ECG signal portion associated with the secondtime based on the first ECG signal portion, determining at least oneerror value based on the predicted ECG signal portion and the second ECGsignal portion, and training the rhythm change classifier (e.g.,updating the weights thereof and/or the like) based on the errorvalue(s) (e.g., using back propagation and/or the like), as describedherein. In some embodiments, the error value(s) may include one of aprediction error or a contrastive loss, as described herein.

In some embodiments, the historical collection of the plurality of ECGsignal portions may include a first ECG signal portion associated with afirst time and a second ECG signal portion associated with a secondtime, as described herein. Additionally or alternatively, heartmonitoring device 102 and/or gateway device 129 may train the rhythmchange classifier by predicting, with the rhythm change classifier, apredicted time associated with the second ECG signal portion based onthe a first ECG signal portion and the second ECG signal, determining atleast one error value based on the predicted time and the second time,and training the rhythm change classifier (e.g., updating the weightsthereof and/or the like) based on the error value(s) (e.g., using backpropagation and/or the like), as described herein. In some embodiments,the error value(s) may include one of a prediction error or acontrastive loss, as described herein.

In some embodiments, there may be an insufficient number of ECG signalportions in the historical collection of the plurality of ECG signalportions with known rhythm change information to train the rhythm changeclassifier to perform the desired task (e.g., detect and/or identify atleast one predetermined rhythm changes), as described herein.Additionally or alternatively, there may be sufficient data (e.g.,historical ECG signal portions and/or the like) to train the rhythmchange classifier to perform a separate task (e.g., which may be relatedin some way to the target task), as described herein. In someembodiments, the rhythm change classifier may be trained to perform theseparate task (e.g., counting R-peaks, determining heart rate, and/orthe like based on the ECG signal(s)), as described herein. Additionallyor alternatively, the rhythm change classifier may then be adapted toperform the target task, as described herein. For example, in someembodiments, the rhythm change classifier may be retrained using thelimited amount of ECG signal portions in the historical collection ofthe plurality of ECG signal portions with known rhythm changeinformation and/or the like, as described herein. Additionally oralternatively, the rhythm change classifier may be used to perform theseparate task (e.g., counting R-peaks, determining heart rate, and/orthe like based on the ECG signal(s)), and the output thereof may beapplied to the target task, as described herein. For example, theprocessor (e.g., of heart monitoring device 102 and/or gateway device129) may detect, with the rhythm change classifier, at least one of acount of peaks or a heart rate based on the at least one ECG signal, asdescribed herein. Additionally or alternatively, the processor (e.g., ofheart monitoring device 102 and/or gateway device 129) may determine thedetected at least one of the count of peaks or the heart rate is above afirst threshold (e.g., tachycardia onset threshold) for the patient orbelow a second threshold (e.g., bradycardia onset threshold) for thepatient (e.g., wherein the second threshold for the patient may be lessthan the first threshold for the patient), as described herein.

In some embodiments, there may be an insufficient number of ECG signalportions associated with (e.g., sensed from and/or the like) theplurality of ECG electrodes of the heart monitoring device 102 in thehistorical collection of the plurality of ECG signal portions with knownrhythm change information to train the rhythm change classifier for theECG signal(s) received form the plurality of ECG electrodes of the heartmonitoring device 302 a, as described herein. Additionally oralternatively, there may be sufficient data (e.g., historical ECG signalportions and/or the like) associated with (e.g., sensed from and/or thelike) a second plurality of ECG electrodes (e.g., electrodes from an ECGdevice separate from the heart monitoring device 102, such as a 12-leadECG sensor, a separate external and/or wearable heart monitoring device,and/or the like) independent of the plurality of ECG electrodes of theheart monitoring device 102 to train the rhythm change classifier basedon the second plurality of ECG electrodes, as described herein. In someembodiments, the rhythm change classifier may be trained based on theECG signal portion(s) associated with (e.g., sensed from and/or thelike) the second plurality of ECG electrodes, as described herein.Additionally or alternatively, the rhythm change classifier may then beadapted to detect the predetermined rhythm change(s) based on theplurality of ECG electrodes of heart monitoring device 102, as describedherein. In some embodiments, heart monitoring device 102 and/or gatewaydevice 129 may determine (e.g., calculate and/or the like) a transform(e.g., vector projection and/or the like) of the ECG signal portion(s)associated with the second plurality of ECG electrodes to the pluralityof ECG electrodes of heart monitoring device 102, and the transform ofthe ECG signal portion(s) may be used to train the rhythm changeclassifier as if the ECG signal portions were associated with (e.g.,sensed from and/or the like) the plurality of ECG electrodes of theheart monitoring device 102, as described herein.

As shown in FIG. 4B, at step 406 b, at least one ECG signal may bereceived, as described herein. For example, heart monitoring device 102and/or gateway device 129 (e.g., the processor(s) thereof) may beconfigured to receive the ECG signal(s). In some embodiments, heartmonitoring device 102 may be configured to receive the ECG signal(s) viathe ECG channel(s), as described herein. Additionally or alternatively,heart monitoring device 102 may be configured to communicate (e.g.,transmit) the ECG signal(s) to gateway device 129, and/or gateway device129 may receive the ECG signals from heart monitoring device 102, asdescribed herein.

As shown in FIG. 4B, at step 408 b, at least one of predetermined rhythmchange and/or a time thereof may be detected. For example, heartmonitoring device 102 and/or gateway device 129 (e.g., the processor(s)thereof) may detect, with the rhythm change classifier, time datacorresponding to a predetermined rhythm change in the at least one ECGsignal, as described herein. Additionally or alternatively, heartmonitoring device 102 and/or gateway device 129 (e.g., the processor(s)thereof) may detect, with the rhythm change classifier, thepredetermined rhythm change based on the at least one ECG signal, asdescribed herein.

In some embodiments, heart monitoring device 102 and/or gateway device129 (e.g., processor(s) thereof) may detect (e.g., with the trainedrhythm change classifier) the predetermined rhythm change based on theat least one ECG signal. In some embodiments, heart monitoring device102 may further include at least one sensor and associated sensorcircuitry configured to sense non-ECG biometric data of the patient(which, in some embodiments, may be communicated to gateway device 129),as described herein. Additionally or alternatively, detecting thepredetermined rhythm change may be further based on the non-ECGbiometric data of the patient (e.g., non-ECG biometric data of thepatient may be input into the neural network(s) of the rhythm changeclassifier, may be combined with the output of the rhythm changeclassifier, and/or the like), as described herein

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one baseline ECG signal portion of thepatient, as described herein.

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one calibration measurement of the patient, asdescribed herein.

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one reference vector of the patient, asdescribed herein.

In some embodiments, detecting the predetermined rhythm change may befurther based on at least one previous ECG signal portion, as describedherein.

In some embodiments, the heart monitoring device 102 and/or gatewaydevice 129 (e.g., processor(s) thereof) may further determine (e.g.,with the rhythm change classifier) a confidence score associated withthe predetermined rhythm change based on the at least one ECG signal, asdescribed herein.

As shown in FIG. 4B, at 410 b, at least one ECG signal portion may bedetermined (e.g., based on the detected time data, the detectedpredetermined rhythm change, and/or the like. For example, heartmonitoring device 102 and/or gateway device 129 (e.g., the processor(s)thereof) may determine (e.g., based on the detected time data) at leastone ECG signal portion associated with the detected time datacorresponding to the predetermined rhythm change in the ECG signal(s),as described herein.

As shown in FIG. 4B, at step 412 b, the ECG signal portion(s) may betransmitted. For example, heart monitoring device 102 and/or gatewaydevice 129 may communicate (e.g., transmit and/or the like) thedetermined ECG signal portion(s) to remote computer system 104, asdescribed herein. Additionally or alternatively, heart monitoring device102 and/or gateway device 129 (e.g., the processor(s) thereof) maycommunicate an indication (e.g., a flag, an indicator, a confidencescore, a mark, metadata, the time data, and/or the like) associated withthe predetermined rhythm change detected (e.g., identified and/or thelike) in the ECG signal portion(s).

In some embodiments, heart monitoring device 102 and/or gateway device129 (e.g., the processor(s) thereof) may further communicate (e.g.,transmit and/or the like) at least one second ECG signal portion of theECG signal(s) to remote computer system 104, as described herein.Additionally or alternatively, the second ECG signal portion(s) may beindependent of the detected time data corresponding to the predeterminedrhythm change in the ECG signal(s), as described herein.

In some embodiments, gateway device 129 may enable communication betweenheart monitoring device 102 and remote computer system 104, as describedherein.

In some embodiments, remote computing system 104 may receive thedetermined ECG signal portion(s) (e.g., from heart monitoring device 102and/or gateway device 129).

As shown in FIG. 4B, at step 414 b, the second ECG signal portion(s)and/or annotation data associated with at least one annotation for thesecond ECG signal portion(s) may be received, as described herein. Forexample, heart monitoring device 102 and/or gateway device 129 mayreceive the second ECG signal portion(s) and/or annotation dataassociated with at least one annotation for the second ECG signalportion(s) from remote computer system 104, as described herein.

In some embodiments, remote computing system 104 may analyze thedetermined ECG signal portion(s) to classify a type of arrhythmia forthe rhythm change(s) in the ECG signal(s), as described herein. Forexample, remote computer system 204 a may include an arrhythmia typeclassifier (e.g., including at least one (second) neural network trainedbased on a (second) historical collection of a (second) plurality of ECGsignal portions with known arrhythmia type information), as describedherein.

In some embodiments, remote computer system 104 may communicate (e.g.,transmit and/or the like) at least one message associated with thedetermined ECG signal portion(s) and/or the type of arrhythmiaassociated with the rhythm change, as described herein. For example, themessage(s) may be communicated from remote computer system 104 totechnician device 108, as described herein.

In some embodiments, remote computer system 104 may transmit at leastone message associated with the second ECG signal portion(s) (e.g.,randomly determined second ECG signal portion(s), second ECG signalportion(s) determined to have a confidence score below a first thresholdand above a second threshold, and/or the like, as described herein) totechnician device 108, as described herein.

In some embodiments, technician device 108 may receive at least oneannotation associated with the ECG signal portion(s), e.g., via inputfrom a user (e.g., a technician and/or the like). Additionally oralternatively, remote computer system 104 may receive annotation dataassociated with the annotation(s) from technician device 108, asdescribed herein.

In some embodiments, remote computer system 104 may communicate thesecond ECG signal portion(s) and/or annotation data associated with atleast one annotation for the second ECG signal portion(s) to heartmonitoring device 102 and/or gateway device 129, as described herein.

As shown in FIG. 4B, at step 416 b, the rhythm change classifier may beretrained. For example, heart monitoring device 102 and/or gatewaydevice 129 may retrain the rhythm change classifier (and/or train anupdated rhythm change classifier) based on the historical collection ofthe plurality of ECG signal portions with the known rhythm changeinformation, the second ECG signal portion(s), and the annotation dataassociated therewith, as described herein.

In some non-limiting embodiments, process 400 b may include repeating atleast some steps (e.g., steps 406 b-412 b, 406 b-416 b, and/or thelike). For example, at least some such steps may be repeatedcontinuously, periodically, and/or the like. For example, ECG signal(s)may be received (406 b) continuously. Additionally or alternatively,received ECG signals may be analyzed using the rhythm change classifiercontinuously. For example, predetermined rhythm changes and/or time dataassociated therewith may be detected (408 b) as often as rhythm changesoccur in the ECG signal(s). Additionally or alternatively, the ECGsignal portion(s) may be determined (410 b) and/or transmitted (412 b)as often as rhythm changes and/or time data associated therewith may bedetected. For example, the annotation(s) (and/or ECG signal portion(s)associated therewith) may be received (414 b) and/or the rhythm changeclassifier may be retrained (416 b) periodically, continuously, and/orthe like.

Referring now to FIG. 4C, FIG. 4C shows an example flow chart of aprocess 400 c for arrhythmia monitoring, according to some embodiments.In some embodiments, one or more of the steps of process 400 c may beperformed (e.g., completely, partially, and/or the like) by remoteserver 104. In some non-limiting embodiments, one or more of the stepsof process 400 c may be performed (e.g., completely, partially, and/orthe like) by another system, another device, another group of systems,or another group of devices, separate from or including remote server104, such as heart monitoring device 102, data repository 106,technician device 108, gateway 129, and/or the like.

As shown in FIG. 4C, at step 402 a, a historical collection of aplurality of ECG signal portions and information related thereto (e.g.,known arrhythmia type information and/or the like) may be received. Forexample, remote computer system 104 may receive (e.g., retrieve, searchfor, send a request and/or query to cause data repository 106 tocommunicate, and/or the like) a historical collection of a plurality ofECG signal portions and information related thereto (e.g., knownarrhythmia type information and/or the like), e.g., from data repository106, as described herein.

As shown in FIG. 4C, at step 404 c, an arrhythmia type classifier may betrained, as described herein. For example, remote computer system 104may train at least one neural network of at least one classifier (e.g.,an arrhythmia type classifier and/or the like) based on the historicalcollection of a plurality of ECG signal portions and information relatedthereto (e.g., known arrhythmia type information and/or the like), asdescribed herein.

In some embodiments, an arrhythmia type classifier may include at leastone neural network (e.g., at least one second neural network), asdescribed herein. Additionally or alternatively, the at least one(second) neural network may include at least one of a deep neuralnetwork, a convolutional neural network, a recurrent neural network, anattention network, a fully connected neural network, any combinationthereof, and/or the like, as described herein. In some embodiments, theneural network(s) may include a plurality of Siamese branches (e.g.,each respective Siamese branch associated with a respective ECGchannel), as described herein.

In some embodiments, remote computer system 104 may train an arrhythmiatype classifier by predicting, with the arrhythmia type classifier, apredicted type of arrhythmia in each respective ECG signal portion ofthe historical collection of the plurality of ECG signal portions (or asecond plurality thereof), determining at least one error value based onthe predicted type of arrhythmia and the known arrhythmia typeinformation (e.g., a respective annotations associated with a known typeof arrhythmia for each respective ECG signal portion), and training thearrhythmia type classifier (e.g., updating the weights thereof and/orthe like) based on the error value(s) (e.g., using back propagationand/or the like), as described herein. In some embodiments, the errorvalue(s) may include one of a prediction error or a contrastive loss, asdescribed herein.

In some embodiments, the known arrhythmia type information may include aplurality of annotations, as described herein. For example, eachannotation may be associated with a respective ECG signal portion of theplurality of ECG signal portions, as described herein. In someembodiments, remote computer system 104 may train the arrhythmia typeclassifier based on the plurality of ECG signals and the plurality ofannotations, as described herein.

In some embodiments, the plurality of annotations may be from aplurality of technicians (e.g., a plurality of technician devices 108and/or the like), as described herein. Additionally or alternatively,each annotation annotations may be associated with a respectivetechnician of the plurality of technicians and/or a respective ECGsignal portion of the plurality of ECG signal portions, as describedherein. In some embodiments, the arrhythmia type classifier may betrained separately for each technician, as described herein. Forexample, for a first technician of the plurality of technicians, thearrhythmia type classifier may be trained (e.g., by remote computersystem 104) based on a subset of the plurality of ECG signals and theplurality of annotations associated with at least one other technicianof the plurality of technicians different than the first technician, asdescribed herein.

In some embodiments, each annotation may be associated with one possibletype of arrhythmia (e.g., a label associated with a possible type ofarrhythmia, a text string identifying at least one possible typearrhythmia, and/or the like) of the respective ECG signal(s) and/orportion(s) thereof, as described herein.

In some embodiments, there may be an insufficient number of ECG signalportions associated with (e.g., sensed from and/or the like) at leastone second ECG electrode in the historical collection of the pluralityof ECG signal portions with known arrhythmia type information (e.g.,annotations, labels, and/or the like) to train the arrhythmia typeclassifier for the ECG signal(s) received from the second ECGelectrode(s), as described herein. Additionally or alternatively, theremay be sufficient data (e.g., historical ECG signal portions and/or thelike) associated with (e.g., sensed from and/or the like) at least onefirst ECG electrode (e.g., electrodes from an ECG device separate fromthe second ECG electrode(s), such as a 12-lead ECG sensor, a separateexternal and/or wearable heart monitoring device, and/or the like)independent of the second ECG electrode(s) to train the rhythm changeclassifier based on the second ECG electrode(s), as described herein. Insome embodiments, the known arrhythmia type information may include aplurality of annotations, each of which may be associated with arespective ECG signal portion of a first plurality of ECG signalportions associated with the first ECG electrode(s), as describedherein. In some embodiments, each respective ECG signal portion of asecond plurality of ECG signal portions associated with the second ECGelectrode(s) may correspond to a respective ECG signal portion of thefirst plurality of ECG signal portions, as described herein. In someembodiments, remote computer system 104 may train the arrhythmia typeclassifier by predicting, with the arrhythmia type classifier, apredicted type of arrhythmia in each respective ECG signal portion ofthe second plurality of ECG signal portions, determining at least oneerror value based on the predicted type of arrhythmia and the respectiveannotation of the plurality of annotations associated with a respectiveECG signal portion of the first plurality of ECG signal portionscorresponding to the respective ECG signal portion of the secondplurality of ECG signal portions, and training (e.g., updating theweights of and/or the like) the arrhythmia type classifier based on theat least one error value (e.g., based on back propagation and/or thelike), as described herein.

In some embodiments, ECG signal portions associated with multipleelectrodes may be combined (e.g., by vector addition, vector projection,a transform, and/or the like) to form extrapolated ECG signal portionsthat may be more familiar and/or suitable for review by a human user(e.g., technician and/or the like), as described herein. For example,the historical collection of the plurality of ECG signal portions mayinclude a first plurality of ECG signal portions of at least one firstECG signal based on first surface ECG activity sensed by at least onefirst ECG electrode and a second plurality of ECG signal portions of atleast one second ECG signal based on second surface ECG activity sensedby at least one second ECG electrode, as described herein. In someembodiments, each ECG signal portion of the first plurality of ECGsignal portions may be combined (e.g., by vector addition, vectorprojection, a transform, and/or the like) with a respective ECG signalportion of the second plurality of ECG signal portions to form aplurality of extrapolated ECG signal portions (e.g., by remote computersystem 104), as described herein. In some embodiments, the knownarrhythmia type information may include a plurality of annotations, asdescribed herein. Additionally or alternatively, each respectiveannotation may be associated with a respective extrapolated ECG signalportion of the plurality of extrapolated ECG signal portions, asdescribed herein.

In some embodiments, at least some of the plurality of ECG signalportions of the historical collection may be time warped (e.g., timedilated and/or the like) to form a plurality of warped ECG signalportions (e.g., by remote computer system 104 using any suitable signalprocessing technique for time warping, time dilation, and/or the like),as described herein.

In some embodiments, at least some of the plurality of ECG signalportions of the historical collection may be at least one of filtered,inverted, any combination thereof, and/or the like (e.g., by remotecomputer system 104), as described herein.

In some embodiments, at least one noise signal portion may be combinedwith at least some of the plurality of ECG signal portions of thehistorical collection (e.g., by remote computer system 104), asdescribed herein.

In some embodiments, at least some of the plurality of ECG signalportions of the historical collection may be style transferred (e.g., byremote computer system 104), as described herein.

As shown in FIG. 4C, at step 406 c, at least one ECG signal andannotation data associated with at least one annotation for each ECGsignal may be received. For example remote computer system 104 mayreceive at least one ECG signal and annotation data associated with atleast one annotation for each ECG signal (e.g., from technician device108), as described herein.

As shown in FIG. 4C, at step 408 c, a type of arrhythmia may be detected(e.g., classified), as described herein. For example, remote computersystem 104 may detect, with the arrhythmia type classifier, a type ofarrhythmia in the ECG signal(s) and time data associated with thedetected type of arrhythmia, as described herein. For example, the timedata may include at least one of a start time, a time interval, anycombination thereof, and/or the like, as described herein. In someembodiments, remote computer system 104 may determine, based on the timedata, at least one ECG signal portion associated with the detected typeof arrhythmia in the ECG signal(s), as described herein.

In some embodiments, remote computer system 104 may determine aplausibility score for the annotation(s) based on the detected type ofarrhythmia. For example, an output of at least one neural network of thearrhythmia type classifier may include a confidence score (e.g., aprobability and/or the like) associated with each possible type ofarrhythmia, as described herein. For example, the type of arrhythmiadetermined by the arrhythmia type classifier may be the type ofarrhythmia with a highest confidence score (e.g., probability and/or thelike). Additionally or alternatively, each annotation may be associatedwith one possible type of arrhythmia (e.g., a label associated with apossible type of arrhythmia, a text string identifying at least onepossible type arrhythmia, and/or the like). In some embodiments,plausibility score of each annotation may be the confidence score (e.g.,determined by the arrhythmia type classifier) of the possible type ofarrhythmia associated with such annotation. In some embodiments, thearrhythmia type classifier may include a plurality of neural networks,and each such neural network may output a confidence score associatedwith at least one possible type of arrhythmia.

As shown in FIG. 4C, at step 410 c, at least one message may begenerated (e.g., based on the determined ECG signal portion, theplausibility score, and/or the like). For example, remote computersystem 104 may generate at least one message based on the at least onedetermined ECG signal portion and the plausibility score for the atleast one annotation, as described herein. For example, the message(s)may indicate at least one of a recommendation to annotate the at leastone determined ECG signal portion based on the detected type ofarrhythmia, a recommendation to reevaluate the annotation dataassociated with the at least one determined ECG signal portion based onthe plausibility score, and/or the like.

In some embodiments, remote computer system 104 may to determine theplausibility score is below a threshold. Additionally or alternatively,generating the message(s) may include remote computer system 104generating, based on the determination that the plausibility score isbelow the threshold, the at least one message indicating therecommendation to reevaluate the annotation data associated with the atleast one determined ECG signal portion, as described herein.

As shown in FIG. 4C, at step 412 c, the message(s) may be transmitted.For example, remote computer system 104 may transmit at least some ofthe message(s) associated with the at least one determined ECG signalportion to technician device 108, as described herein. Additionally oralternatively, remote computer system 104 may transmit at least some ofthe message(s) associated with the at least one determined ECG signalportion to supervisor device 110, as described herein.

In some non-limiting embodiments, process 400 c may include repeating atleast some steps (e.g., steps 406 c-412 c, 402 c-412 c, and/or thelike). For example, at least some such steps may be repeatedcontinuously, periodically, and/or the like. For example, ECG signal(s)and/or annotation(s) associated therewith may be received (406 c)continuously, as often as the technician(s) provide such annotation(s),periodically and/or the like. Additionally or alternatively, the type ofarrhythmia may be classified (408 c) and/or the message(s) may begenerated (410 c) and/or transmitted (412 c) continuously, as often asECG signal(s) and/or annotation(s) are received, periodically, and/orthe like. For example, the historical collection of ECG signal portionsmay be updated (e.g., new ECG signal portion(s) and/or known arrhythmiatype information may be added and/or the like) continuously,periodically, and/or the like. Additionally or alternatively, thearrhythmia type classifier may be (re-)trained (404 c) continuously, asoften as the historical collection of ECG signal portions is updated,periodically and/or the like.

Referring now to FIG. 5A, FIG. 5A shows an example diagram of a neuralnetwork 500 a of an exemplary rhythm change classifier. As shown in FIG.5A, neural network 500 a may include at least one input layer 502 a, asdescribed herein. Additionally or alternatively, neural network 500 amay include at least one output layer 508 a, as described herein. Insome embodiments, neural network 500 a may include at least one hiddenlayer (e.g., first hidden layer 504 a, last hidden layer 506 a, and/orthe like), as described herein. In some embodiments, the hidden layer(s)(e.g., first hidden layer 504 a, last hidden layer 506 a, and/or thelike) may include a plurality of convolutional layers, as describedherein.

Referring now to FIG. 5B, FIG. 5B shows an example diagram of a neuralnetwork 500 b of an exemplary arrhythmia type classifier. As shown inFIG. 5B, neural network 500 b may include at least one input layer 502b, as described herein. Additionally or alternatively, neural network500 b may include at least one output layer 508 b, as described herein.In some embodiments, neural network 500 b may include at least onehidden layer (e.g., first hidden layer 504 b, last hidden layer 506 b,and/or the like), as described herein. In some embodiments, the hiddenlayer(s) (e.g., first hidden layer 504 b, last hidden layer 506 b,and/or the like) may include a plurality of convolutional layers, asdescribed herein.

Referring now to FIGS. 6A-E, FIGS. 6A-E show example ECG signalportions. As shown in FIG. 6A, first ECG signal portion 600 a mayinclude portions of first ECG signal from a first channel 602 a andsecond ECG signal from a second channel 604 a. In some embodiments,first ECG signal portion 600 a may show normal sinus rhythm (NSR) onboth channels.

As shown in FIG. 6B, second ECG signal portion 600 b may includeportions of first ECG signal from a first channel 602 b and second ECGsignal from a second channel 604 b. In some embodiments, second ECGsignal portion 600 b may show a rhythm change between 18 and 19 secondson both channels.

As shown in FIG. 6C, third ECG signal portion 600 c may include portionsof first ECG signal from a first channel 602 c and second ECG signalfrom a second channel 604 c. In some embodiments, third ECG signalportion 600 c may show a therapeutic treatment (e.g., therapeutic shockof 150 J) between 62 and 63 seconds on both channels.

As shown in FIG. 6D, fourth ECG signal portion 600 d may includeportions of first ECG signal from a first channel 602 d and second ECGsignal from a second channel 604 d. In some embodiments, fourth ECGsignal portion 600 d may show a rhythm change in the highlighted timeperiod 606 d on both channels.

As shown in FIG. 6E, fifth ECG signal portion 600 e may include aportion of an ECG signal from a channel 604 e. In some embodiments,fifth ECG signal portion 600 e may show a rhythm change between 93 and94 seconds.

Referring now to FIG. 7 , FIG. 7 is a diagram of example components of adevice 700. Device 700 may correspond to one or more devices of heartmonitoring device 102, remote computer system 104, data repository 106,technician device 108, supervisor device 110, and/or gateway device 129.In some non-limiting embodiments, heart monitoring device 102, remotecomputer system 104, data repository 106, technician device 108,supervisor device 110, and/or gateway device 129 may include at leastone device 700 and/or at least one component of device 700. As shown inFIG. 7 , device 700 may include bus 702, processor 704, memory 706,storage component 708, input component 710, output component 712, andcommunication interface 714.

Bus 702 may include a component that permits communication among thecomponents of device 700. In some non-limiting embodiments, processor704 may be implemented in hardware, firmware, or a combination ofhardware and software. For example, processor 704 may include aprocessor (e.g., a central processing unit (CPU), a graphics processingunit (GPU), an accelerated processing unit (APU), and/or the like), amicroprocessor, a digital signal processor (DSP), and/or any processingcomponent (e.g., a field-programmable gate array (FPGA), anapplication-specific integrated circuit (ASIC), and/or the like), and/orthe like, which can be programmed to perform a function. Memory 706 mayinclude random access memory (RAM), read only memory (ROM), and/oranother type of dynamic or static storage device (e.g., flash memory,magnetic memory, optical memory, and/or the like) that storesinformation and/or instructions for use by processor 704.

Storage component 708 may store information and/or software related tothe operation and use of device 700. For example, storage component 708may include a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, a solid state disk, and/or the like), a compact disc(CD), a digital versatile disc (DVD), a floppy disk, a cartridge, amagnetic tape, and/or another type of computer-readable medium, alongwith a corresponding drive.

Input component 710 may include a component that permits device 700 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, amicrophone, a camera, and/or the like). Additionally or alternatively,input component 710 may include a sensor for sensing information (e.g.,a global positioning system (GPS) component, an accelerometer, agyroscope, an actuator, and/or the like). Output component 712 mayinclude a component that provides output information from device 700(e.g., a display, a speaker, one or more light-emitting diodes (LEDs),and/or the like).

Communication interface 714 may include a transceiver-like component(e.g., a transceiver, a receiver and transmitter that are separate,and/or the like) that enables device 700 to communicate with otherdevices, such as via a wired connection, a wireless connection, or acombination of wired and wireless connections. Communication interface714 may permit device 700 to receive information from another deviceand/or provide information to another device. For example, communicationinterface 714 may include an Ethernet interface, an optical interface, acoaxial interface, an infrared interface, a radio frequency (RF)interface, a universal serial bus (USB) interface, a Wi-Fi® interface, aBluetooth® interface, a Zigbee® interface, a cellular network interface,and/or the like.

Device 700 may perform one or more processes described herein. Device700 may perform these processes based on processor 704 executingsoftware instructions stored by a computer-readable medium, such asmemory 706 and/or storage component 708. A computer-readable medium(e.g., a non-transitory computer-readable medium) is defined herein as anon-transitory memory device. A non-transitory memory device includesmemory space located inside of a single physical storage device ormemory space spread across multiple physical storage devices.

Software instructions may be read into memory 706 and/or storagecomponent 708 from another computer-readable medium or from anotherdevice via communication interface 714. When executed, softwareinstructions stored in memory 706 and/or storage component 708 may causeprocessor 704 to perform one or more processes described herein.Additionally or alternatively, hardwired circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, embodiments described herein are notlimited to any specific combination of hardware circuitry and software.

The number and arrangement of components shown in FIG. 7 are provided asan example. In some non-limiting embodiments, device 700 may includeadditional components, fewer components, different components, ordifferently arranged components than those shown in FIG. 7 .Additionally or alternatively, a set of components (e.g., one or morecomponents) of device 700 may perform one or more functions described asbeing performed by another set of components of device 700.

Referring now to FIG. 8 , FIG. 8 shows an exemplary heart monitoringdevice, e.g., an arrhythmia and fluid monitoring system that includes aphysiological monitoring device 810, hereinafter referred to as“sensor(s)”, and a wearable patch 860 configured to place the sensor(s)on, or in the vicinity of, a surface of a body (e.g., a patient).Further, the system may include a portable data transmission device(gateway) 830 that is capable of continuously transmitting data acquiredby the sensor(s) 810 to a remote computer system (e.g., one or moreservers 850) for processing and/or analysis. Thus, for example, thegateway device 830 may transmit to the server 850 data received from thesensor(s) 810 with little or no delay or latency. To this end, in thecontext of data transmission between the device(s) 810 and server(s)850, “continuously” for the present disclosure includes continuous(without interruption), or near continuous, i.e., within one minuteafter completion of a measurement by and/or an occurrence of an event onthe device. Continuity may also be achieved by repetitive successivebursts of transmission, e.g., high-speed transmission. Similarly, theterm “immediate,” according to the present disclosure, includes asoccurring or done at once, or near immediate i.e., within one minuteafter the completion of a measurement by and/or an occurrence of anevent occurring on the device.

Further, in the context of physiological data acquisition by thedevice(s) 810, “continuously” also includes uninterrupted collection ofsensor data, such as ECG data and/or accelerometer data, with clinicalcontinuity. In this case, short interruptions in data acquisition of upto 1-second several times an hour or longer interruptions of a fewminutes several times a day may be tolerated and can still be seen as“continuous”. As to latency as a result of such a continuous scheme asdescribed herein, this relates to the overall budget of response timewhich can amount to between about 5 to about 15 minutes overall responsetime (e.g., time from when an event onset is detected to when anotification regarding the event is issued). As such,transmission/acquisition latency would therefore be in the order ofminutes.

Further, the wearable devices described herein are configured forlong-term and/or extended use or wear by, or attachment or connection toa patient. For example, devices as described herein may be capable ofbeing used or worn by, or attached or connected to a patient, withoutsubstantial interruption, for example, up to 24 hours or beyond (e.g.,weeks, months, or even years). In some implementations, such devices maybe removed for a period of time before use, wear, attachment, orconnection to the patient is resumed, e.g., to change batteries, carryout technical service, update the device software or firmware, and/or totake a shower or engage in other activities, without departing from thescope of the examples described herein.

In some embodiments, the transmission of data/signals 820 between thesensor(s) 810 and the gateway device 830 may be a one way (e.g., fromthe sensor(s) 810 to the gateway device 830) or the transmission may bebi-directional. Similarly, the transmission of data/signals 840 betweenthe gateway device 830 and the server 850 may be one way (e.g., from thegateway device 830 to the server 850) or bi-directional. The system mayalso include a charger (not shown) for powering the electronics of thesystem.

In some embodiments, the sensor(s) 810 is configured to monitor, recordand transmit to the gateway device 830 physiological data about thewearer of the sensor(s) 810 continuously. In particular, the sensor(s)810 may not interrupt monitoring and/or recording additional data whiletransmitting already acquired data to the gateway device 830. Putanother way, in some embodiments, both the monitoring/recording and thetransmission processes occur at the same time or at least nearly at thesame time.

As an another example, if the sensor(s) 810 does suspend monitoringand/or recording additional data while it is transmitting alreadyacquired data to the gateway device 830, the sensor(s) 810 may thenresume monitoring and/or recording additional data prior to all thealready acquired data being transmitted to the gateway device 830. Inother words, the interruption period for monitoring and/or recording maybe less in comparison to the time it takes to transmit the alreadyacquired data (e.g., between about 0% to about 80%, about 0% to about60%, about 0% to about 40%, about 0% to about 20%, about 0% to about10%, about 0% to about 5%, including values and subranges therebetween),facilitating the near-continuous monitoring and/or recording ofadditional data during transmission of already acquired physiologicaldata. For example in one specific scenario, when a measurement timeduration is around 2 minutes, any period of suspension or interruptionin the monitoring and/or recording of subsequent measurement data mayrange from a just few milliseconds to about a minute. Example reasonsfor such suspension or interruption of data may include allowing for thecompletion of certain data integrity and/or other on-line tests ofpreviously acquired data as described in further detail below. If theprevious measurement data has problems, the sensor(s) 810 can notify thepatient and/or remote technician of the problems so that appropriateadjustments can be made.

In some embodiments, the bandwidth of the link 820 between the sensor810 and the gateway device 830 may be larger, and in some instancessignificantly larger, than the bandwidth of the acquired data to betransmitted via the link 820 (e.g., burst transmission). Suchembodiments ameliorate issues that may arise during link interruptions,periods of reduced/absent reception, etc. In some embodiments, whentransmission is resumed after interruption, the resumption may be in theform of last-in-first-out (LIFO). The gateway device 830 can beconfigured to operate in a store and forward mode where the datareceived from the sensor 810 is first stored in an onboard memory of thegateway device and then forwarded to the external server. For example,such a mode can be useful where the link with the server may betemporarily unavailable. In some embodiments, the gateway device 830 canfunction as a pipe line and pass through data from the sensor 810immediately to the server. In further examples, the data from the sensormay be compressed using data compression techniques to reduce memoryrequirements as well as transmission times and power consumptions.

In some embodiments, the sensor(s) 810 may be configured to monitor,record and transmit some data in a continuous or near-continuous manneras discussed above, while monitoring, recording and transmitting someother data in a non-continuous manner (e.g., periodically,no-periodically, etc.). For example, the sensor(s) 810 may be configuredto record and transmit ECG data continuously or nearly continuouslywhile radio-frequency (RF) based measurements and/or transmissions maybe periodic. For example, ECG data may be transmitted to the gatewaydevice 830 (and subsequently the server 850) continuously ornear-continuously as additional ECG data is being recorded, whileRF-based measurements may be transmitted once the measuring process iscompleted.

Monitoring and/or recording of physiological data by the sensor(s) 810may be periodic, and in some embodiments, may be accomplished asscheduled (i.e., periodically) without delay or latency during thetransmission of already acquired data to the gateway device 830. Forexample, the sensor(s) 810 may acquire physiological data from thepatient (i.e., the wearer of the sensor(s) 810) in a periodic manner andtransmit the data to the gateway device 830 in a continuous manner asdescribed above.

The sensor(s) 810 may be configured to transmit the acquired data to theservers 850 instead of, or in addition to, transmitting the data to thegateway device 830. The sensor(s) 810 may also be configured to storesome or all of the acquired physiological data. In some embodiments, thetransmission of data from the sensor(s) 810 to the gateway device 830may be accomplished wirelessly (e.g., Bluetooth®, etc.) and/or via awired connection, e.g., 820. The transmission of data from the gatewaydevice 830 to the server 850 may also be accomplished wirelessly (e.g.,Bluetooth®-to-TCP/IP access point communication, Wi-Fi®, cellular, etc.)and/or via a wired connection, e.g., 840.

As mentioned above, in some embodiments, the transmission of data and/orsignals occurs via two links 820, 840, the links between the sensor(s)810 and the gateway device 830 (e.g., Bluetooth® link) and between thegateway device 830 and the server 850 (e.g., Wifi®, cellular). TheBluetooth® link can be a connection bus for sensor(s) 810 and server 850communication, used for passing commands, information on status of themicroprocessor of the sensor(s) 810, measurement data, etc. In someembodiments, the microprocessor of the sensor(s) 810 may initiatecommunication with the server 850 (and/or the gateway device 830), andonce connection is established, the server 850 may be configured toinitiate some or all other communications. In some embodiments, thegateway device 830 may be configured to conserve the power available tothe sensor(s) 810, device 830 and/or servers 850. For example, one orboth links 820, 840 may enter power saving mode (e.g., sleep mode,off-state, etc.) when the connections between the respectivedevices/server are not available. As another example, the transmissionof data may also be at least temporarily interrupted when the linkquality (e.g., available bandwidth) is insufficient for at least asatisfactory transmission of the data. In such embodiments, the gatewaydevice 830 may serve as a master device in its relationship to one orboth of the sensor(s) 810 and the server 850.

In some embodiments, the gateway device 830 may be considered as asimple pipe, the sensor-gateway device-server path may be defined as asingle link, i.e., the link performance may depend on the bottleneckbetween the sensor-gateway device and gateway device-server links. Insome embodiments, at least the main bottleneck may be the gatewaydevice-server link, since the gateway device is carried by the patientin close proximity to the device, while the gateway device-server link(e.g., cellular or WiFi® coverage) is expected to be variable. In someembodiments, a “best effort delivery” quality-of-service may besufficient for the Bluetooth link and/or the TCP/IP link, since thetransmitted data is processed (with some latency, for example) and isused for displaying notifications (for example, instead of beingpresented online to a monitoring center). In some embodiments, a singlegateway device 830 may be configured to serve a plurality of sensors,i.e., the plurality of sensors may be connected to a single gatewaydevice 830 via respective links. In some embodiments, there may be aplurality of gateway devices serving one or more sensor(s), i.e., eachsensor of one or more sensors may be connected to a plurality of gatewaydevices via respective links.

In some embodiments, the transmission links 820, 840 may be configuredto withstand co-existence interference from similar devices in thevicinity and from other devices using the same RF band (e.g.,Bluetooth®, Cellular, WiFi®). Standard Bluetooth® protocol and/orstandard TCP/IP protocols, as well as the addition of cyclic redundancycheck to the transmitted data may be used to address any issue ofinterference. Further, to preserve the security of wireless signals anddata, in some embodiments, data transfer between the sensor and theserver may be done using a proprietary protocol. For example, TCP/IPlink may use SSL protocol to maintain security, and the Bluetooth® linkmay be encrypted. As another example, UDP/HTTP may also be used forsecure transmission of data. In some embodiments, only raw binary datamay be sent, without any patient identification.

Examples of the types of physiological data that the arrhythmia andfluid monitoring sensor(s) 810 is configured to monitor and/or acquirefrom a patient wearing the sensor(s) 810 include one or more ofelectrocardiogram (ECG) data, thoracic impedance, heart rate,respiration rate, physical activity (e.g., movement) and patientposture. In some embodiments, the physiological data may be acquiredand/or transmitted to the gateway device 830 or the server 850 by thesensor(s) 810 in a manner that is continuous, periodic or as instructedby received signals (e.g., as instructed by signal received from thegateway device 830 and/or the server 850). For example, the wearer ofthe sensor or another party (e.g., a health professional) may activatethe sensor(s) 810 and the sensor may start monitoring and/or recordingany one of the above-noted physiological parameters automaticallywithout further input from the wearer or the party. The sensor(s) 810,or the arrhythmia and fluid monitoring system in general, may requestfurther input (e.g., selection of a setting identifying thephysiological parameter to be measured) before initiating the monitoringand/or recording of physiological data. In any case, once the monitoringand/or recording starts, the sensor(s) 810 may transmit the acquireddata to the gateway device 830 and/or the server 850 in an at least acontinuous manner as described above, for example.

In some embodiments, one or more of the above-noted physiologicalparameters may be measured periodically, and the sensor(s) 810 maytransmit the measurements to the gateway device 830 in an at least acontinuous manner as acquired. For example, the periodic measurementsmay proceed as scheduled and the transmission to the gateway device 830may occur with little or no delay or latency after data is acquired.

In some embodiments, the sensor(s) 810, or the arrhythmia and fluidmonitoring system in general, may be configured to operate some, but notall, of the available features discussed above. For example, the sensors810 may be configured to monitor and/or acquire one or more of ECG data,thoracic impedance, heart rate, respiration rate, physical activity(e.g., movement), patient posture, etc., but not the others. Forinstance, the sensors may be configured to monitor and/or acquire datasuch as ECG data, but not respiration rate, physical activity (e.g.,movement), patient posture. Such embodiments may be effected, forexample, by including controls in the sensors and/or the system thatseparately control components of the sensors/system responsible for thefeatures. For example, the arrhythmia and fluid monitoring system mayinclude controls (e.g., power buttons) that separately control theaccelerometer and the ECG components of the sensor. By switching on theaccelerometer power control and switching off the ECG power control, insome embodiments, one may allow the monitoring and/or acquiring of datarelated to respiration rate, physical activity, and patient posturewhile deactivating the monitoring and/or acquiring of ECG data.

In some embodiments, an adhesive patch 860 may be used to attach thesensor(s) 810 to a surface of the body of a patient. FIGS. 9A-E show thesensor 970 disclosed herein, a patch 910 configured to attach the sensor970 to a patient's body or at least hold the sensor 970 in proximity toskin of the body, and an illustration of a method of attaching thesensor 970 to the patch 910, according to some embodiments. The patch910 may include a patch frame 930 (e.g., plastic frame) delineating theboundary of the region of the patch 910 that is configured for housingthe sensor 970. The patch 910 may be disposable (e.g., single- orfew-use patches), and may be made of biocompatible, non-woven material.In some embodiments, the sensor 970 may be designed for long-term usage.In such embodiments, the connection between the patch 910 and the sensor970 may be configured to be reversible, i.e., the sensor 970 may beconfigured to be removably attached to the patch 910. For example, thesensor 970 may include components such as snap-in clips 940 that areconfigured to secure the sensor 970 to the patch 910 (e.g., the patchframe 930) upon attachment (and released the sensor 970 from the patchwhen separation is desired). The sensor 970 may also include positioningtabs 960 that facilitate the attachment process between the sensor 970and the patch 910. In some embodiments, the patch may be designed tomaintain attachment to skin of a patient for several days (e.g., in therange from about 4 days to about 10 days, from about 5 days to about 7days, etc.).

In some embodiments, the patch 910 may include additional componentsthat facilitate or aid with the monitoring and/or recording or acquiringof physiological data by the sensor 970. For example, the patch mayinclude conductive elements such as one or more ECG electrodes 920(e.g., a single lead, two leads, etc.) that can be used when recordingECG data from the surface (e.g., skin contacted directly or through acovering) of a patient's body. The electrodes may be coupled to thesensor 970 by dedicated wiring within the patch. In some embodiments,the ECG may have a sampling rate in then range from about 950 Hz toabout 500 Hz, from about 300 Hz to about 450 Hz, from about 350 Hz toabout 400 Hz, including values and subranges therebetween. In someembodiments, the ECG signal may be sampled after band-pass filtering bya 12 bit ADC. During normal operation, data may be transferred to theserver “as-is” and can then be used by the server algorithms foranalysis. In some embodiments, an internal algorithm allows forreal-time evaluation of the ECG signal quality upon each attachment ofthe device to the patient (“attachment test”).

Examples of locations on surface of a patient body at which a patch maybe placed are shown in FIGS. 9D-E, where a patch 1020 housing sensor1010 is shown as placed at on the side (below armpit, for example) (FIG.9D) and upper chest (FIG. 9E) of the torso of a patient. It is to benoted that the patch may be placed on any part of the surface of apatient's body that allows for efficient monitoring and recording of aphysiological data (e.g., area of skin that allows for uniformattachment of the patch 1020 to the skin). For example, one may placethe patch 1020 under an armpit at the nipple level for performing lungfluid level measurements. With respect to ECG measurements, the ECGsignal at this location may be represented as the difference betweenstandard V5 and V6 leads of an ECG.

With reference to FIGS. 10A-C, in some embodiments, front, back andexploded views, respectively, of the sensor(s) disclosed herein areshown. FIG. 10A shows the front 1012 and back 1014 covers of the sensor1010 (labelled as top and bottom covers 1070 in FIG. 10C). In someembodiments, such covers may couple to each other to seal the electricalcomponents of the sensor from the surrounding environment (e.g.,electrical sealing). In such embodiments, metallic tabs 1025 mayprotrude outside the covers to provide electrical connection forsituations such as performing ECG measurements, charging power sourceand/or the like.

FIG. 10B shows that the sensor 1010 may include one or more indicatorsthat identify the status of the sensor 1010 to the user of the sensor1010. Examples of such indicators include but are not limited to lightindicator 1040 (e.g., a light emitting diode (LED) indicator) and soundindicators 1020. In some embodiments, the indicators 1020, 1040 providefeedback on the status of the sensor 1010 and components thereof, suchas the charging and/or power level of the power source of the sensor1010 (e.g., a battery), the attachment level of the sensor 1010 to thepatch 910, the attachment level of the patch 910 to the surface of thebody to which the patch 910 is attached, etc. As another example, thesensor may respond by blinking (e.g., via the light indicator 1040) orbuzzing (e.g., via the sound indicator 1020) in response to anengagement by a patient to indicate possible symptoms.

In some embodiments, FIG. 10C provides an exploded view of the sensor1010 depicting at least some of the components of the sensor. Forexample, the sensor 1010 may comprise a power source such as a battery1080, a light indicator 1060, a button 1050 for facilitating theinteraction of a patient, a healthcare provider, and/or a technicianwith the sensor, a wireless communications circuit 1085, a radiofrequency shield 1090 (such as a metallic cover, e.g., to preventinterferences with the ECG processing and other digital circuitry), adigital circuitry board 1095, and/or the like. FIG. 10C shows aBluetooth unit as an example of a wireless communications circuit 1085,although in addition to or alternatively to the Bluetooth unit, othermodules facilitating other types of communications (examples of whichincluding WiFi®, cellular, etc.) may be included in the sensor 1010.

In some embodiments, the sensor 1010 may also include input interfacessuch as buttons for interfacing with a user. For example, the sensor mayinclude a button 1030 that allows a patient or a health careprofessional to activate or deactivate the sensor 1010. Such inputinterfaces may be configured to avoid or at least minimize unintendedinteractions with a user. For example, a button may be sized and shapedto avoid accidental activation (e.g., the button may be configured torequire activation by being pushed in with an external object). Thisbutton may be used to reset the sensor as well as pair the sensor to thegateway device and initiate communication. In some embodiments, theinput interface of the sensor may include a touch screen configured toreceive input from a user and/or provide information back to the user.For example, the input may allow the user to set the sensor in an“airplane mode,” i.e., for example by deactivating any wirelesscommunication (e.g., Wi-Fi, Bluetooth, etc.) with external devicesand/or servers. For example, the button can be implemented as a magneticswitch, e.g., an embedded magnetic switch, instead of a physical button.Such an implementation can be useful for designing the housing of thedevice and avoid exposing button components to the environment.

In some embodiments, as described above, the disclosed sensor isconfigured to monitor and/or acquire data on physiological parametersincluding but not limited to electrocardiogram (ECG) data, thoracicimpedance, heart rate, respiration rate, physical activity, postureand/or the like. To that effect, the sensor and/or the patch housing thesensor may include components that facilitate or undertake themonitoring and/or recording of at least some of these parameters. Forexample, as noted above, the patch housing the sensor may include ECGelectrodes coupled to the sensors to facilitate the monitoring and/oracquiring of ECG data. As shown in FIG. 11A, which shows an exampleembodiment of device electronics architecture for measurements andtransmission of patient physiological data, the sensor includes EGGprocessing circuitry configured to couple to the ECG electrodes embeddedin the patch housing the sensor itself. The ECG processing circuitry isconfigured to, for example, perform filtering, amplification, and/orremoval of noise, low frequency variations in the signal, and othersignal artifacts.

As another example, the sensor may include radio frequency (RF) antennafor directing electromagnetic waves into a body of a patient andreceiving waves that are scattered and/or reflected from internaltissues. Further, the sensor may include RF circuitry or moduleconfigured to process the received waves so as to determine someproperties of the tissues that are on the path of the transmitted and/orscattered/reflected waves. For example, the antenna may direct RF wavestowards a lung of a patient and the RF circuitry may analyze thescattered/reflected waves to perform an RF-based measurement of the lungfluid level of the patient. FIG. 11A shows an example embodiment of asensor comprising RF antennas, an RF module and circuits for controllingthe module (e.g., field-programmable gate array (FPGA) circuits).

With reference to FIG. 11A, in some embodiments, the sensor 1100includes external interfaces such as but not limited to RF antennas(e.g., bi-static) 1104 a, 1104 b for transmitting & receiving RFsignals, a button or switch 1124 for activating or deactivating thesensor 1100, an LED 1118 and a buzzer 1126 for providing light and audiofeedback to a user of the sensor 1100, a battery charging link 1130coupled to a power management module 1110 for charging an onboard powersource such as a battery 1112, and ECG pads 1130 for recordingsynchronization signal. In some embodiments, the sensor 1100 may alsoinclude a wireless link (e.g., Bluetooth®) (not shown) to provide anexternal server access to the sensor 1100 so as to exert at least somecontrol on the sensor 1100.

Internally, in some embodiments, the sensor 1100 may include amicroprocessor 1108 (which may be alternatively referred to as amicro-controller) that includes instructions thereon specifying howmeasurements (RF, ECG, accelerometer, etc.) are taken and the obtaineddata are transmitted, how to relay the status of the sensor 1100,how/when the sensor 1100 can enter the plurality of sleep levels, and/orthe like. In some embodiments, the instructions may also specify theconditions for performing certain types of measurements. For example,the instructions may specify that the accelerometer may not commencemeasurements (for physical activity, and patient posture, for example)unless the user of the sensor is at rest or maintaining a certainposture. As another example, the instructions may identify theconditions that may have to be fulfilled before ECG measurements cancommence, such conditions including at least sufficient attachment levelbetween the sensor and the surface on the body to which the sensor 1100is attached. In some embodiments, the microprocessor 1108 may haveinternal and external non-volatile memory banks that can be used forkeeping measurement directory and data, scheduler information, and/or alog of actions and errors. This non-volatile memory allows saving powervia a total power-down while retaining data and status information.

FIGS. 11B and 11C are block diagrams that illustrate examples of RFsensor functionality disposed within an RF module (e.g., RF module 1132)according to some embodiments. As noted herein, such functionality maybe used for RF based fluid monitoring of fluid accumulation/content intissue in accordance with the techniques described herein. Referringfirst to FIG. 11B, initially, one or more RF signals (e.g., a single“LO” signal, or different “LO₁” and “LO₂” signals, collectively “LO”signals) can be generated by a broadband synthesizer 1180 (e.g., a pulsegenerator and synthesizer—LO). Such a synthesizer 1180 can preferablyinclude moderate phase noise performance and fast settling timecapabilities (in some embodiments, one or the other). The RF moduleincludes a transceiver portion 1181, including a transmitting antenna(Tx) and associated circuitry for transmitting RF waves directed, forexample, towards a tissue of interest in the patient's body, and areceiver portion 1182, including a receiver antenna (Rx) and associatedcircuitry 1182 for receiving reflected RF waves from, for example, thetissue of interest in the patient's body.

The LO signal at the transceiver (Tx) of the transmitter portion 1181 ismultiplied with an external sine wave at a low frequency intermediatefrequency (IF) signal, generated by an IF source 1184, and directed tothe output of the transceiver (Tx). As noted above, the LO signal attransceiver portion 1181 and the receiver portion 1182 can be generatedby one or two LO sources (e.g., synthesizer(s) 1180). Output power canbe controlled via digital control of a digitally controlled attenuator(DCA) on the RF transceiver path. An external reflected RF wavereturning to a receiving antenna (Rx) is directed to the receiverportion and down-converted to an IF frequency by a down conversionmixer. The reflection characteristics (phase and amplitude) can betransformed to a new IF carrier (e.g., on the order of 250 KHz),filtered and amplified before the ADC 1185.

Digital control for the functionality in FIG. 11B may be achieveddirectly by a processor and/or digital logic (e.g., an FPGA 1186), whichmay be configured to control both the transceiver's configurationprocess, IF signal adjustments and associated switching.

Referring now to FIG. 11C, in some embodiments, the RF module 1132 maybe implemented using a transmitting portion 1187 and receiver portion1190 as shown. For example, the transmitting portion 1188 can include apulse generator 1188 and a transmitting antenna Tx 1189 for transmittingthe RF waves directed towards a tissue of interest in the patient'sbody. the receiver portion 1190 may include a receiving antenna Rx 1191,a low-noise RF amplifier 1192, a receiver 1193 that converts thereflected RF signals to an IF signal by using mixer and local oscillator1194, which may be a monostatic (sheared LO) or a bi-static system. Thesignal can be filtered, amplified and fed in to a detector 1195, theoutput of which may be connected to additional circuitry for furthersignal processing.

With respect to potential RF/ECG interference, in some embodiments thefollowing steps can be taken:

-   -   Ground Separation between digital and RF components: may be        achieved by separating the digital and RF grounds, and utilizing        a single connection point through ferrite bead.    -   RF module shielding may also be used which may comprise a        metallic cover, for example, radio frequency shield 1090 as        shown in FIG. 10C.    -   Power circuitry considerations: different power paths may be        utilized for different components/modules. Additionally, the        power circuit may include filters to avoid noise.    -   ECG filtering may also be used to aid in minimizing RF        interference which prevents high frequency signals interfering        with the ECG circuitry/module.    -   Circuitry layout: ECG signal paths are physically separated from        RF paths. In some embodiments, the ECG signal paths can also be        physically separated from other lines that might interfere.

FIG. 11C shows an example general architecture of the RF module with lowfrequency IF and shared local oscillator (LO). As an examplenon-limiting example, with reference to FIG. 11C, the transmitted RFsignal may be mixed with the IF signal (e.g., about 250 KHz) beforetransmission, so the transmission is actually 2 tones around the carrierRF signal, separated by about 500 KHz.

In some embodiments, the RF module 1132 may include a calibration path(e.g., an electric reflector such as but not limited to a resistor onboard) which generates a steady and constant or near-constant reflectionuncorrelated with the external propagation path. This reflectorgenerates a reflection profile with minimal dependencies to temperature,system noise and device location on the body.

In some embodiments, the RF module 1132 itself may not have anyprocessing components inside. For example, it may be controlled by afield-programmable gate array (FPGA) that defines in each or nearly eachfrequency point one or more of the frequency, output power levels,system gain, bypassing modes and/or enable/disable transmissions.

In some embodiments, the RF module 1132 may support different types ofwaveform configurable options, including but not limited to normaloperation, calibration frame operation, interleaved switching betweennormal and calibration frame operation, interleaved switching betweennormal and delayed path operation, and clear channel sensing. In some ofthese options, for example the normal and interleaved switching ones,the attenuation may be different per frequency, while in the case ofclear channel sensing, there may not be any transmission. For thecalibration frame operation, the attenuation can be the same for allfrequencies but may be higher when compared to those of the normaloperation.

In some embodiments, the transmit (Tx) and receive (Rx) switches may berespectively set to transmit and receive through a calibration path forthe case of calibration frame operation, while for the clear channelsensing, Rx switch may be set to antenna and Tx to calibration path. Forinterleaved switching between normal and calibration frame operationsand between normal and delayed path operations, in some embodiments, theTx and Rx switches may alternate between calibration and antenna pathper frequency, and normal and delayed path, respectively.

In some embodiments, the RF waves may be in the frequency ranges fromabout 100 MHz to about 1 GHz, 200 MHz to about 2.5 GHz, from about 200MHz to about 3 GHz, from about 500 MHz to about 5 GHz, including valuesand subranges therebetween. In some embodiments, a thoracic fluidcontent (TFC) sensitivity may be configured to allow measurement ofheart signals at distances up to about 25 cm, about 20 cm, about 15 cm,about 10 cm, about 5 cm, including values and subranges therebetween,inside the body onto which the disclosed sensor is attached. In someembodiments, the dynamic range is no less than 100 dB, measured in thepresence of a strong coupling signal between transmission & reception.Further the waveform may be stepped frequency (16-128 frequencies),arbitrary with 1 MHz accuracy & resolution. In some embodiments, actualfrequencies selected may be contiguous or not, depending on regulatoryrequirements. In some embodiments, the dwell and settling times may beconfigurable to allow 16-128 frequencies within less than 5 to 20 ms,respectively.

Details on RF-based measurements of physiological parameters such asthoracic fluid content have been discussed in U.S. Patent PublicationNo.: US 2011/0130800, filed Apr. 14, 2010, titled “Methods and Systemsfor Determining Fluid Content of Tissue”; and PCT International PatentPublication No.: WO 2012/011066, filed Jul. 21, 2011, titled“Implantable Dielectrometer,” the disclosures of which are incorporatedby reference herein in their entireties.

It has been noted above that the sensor may include indicators providinginformation on the attachment level of the patch housing the sensor to askin of the wearer of the sensor. Such information may be obtained fromRF-based measurements as discussed in PCT International PatentPublication No.; WO 2016/115175, filed Jan. 12, 2016, titled “Systems,Apparatuses, and Methods for Radio Frequency-Based Attachment Sensing,”the disclosure of which is incorporated by reference herein in itsentirety.

In some embodiments, the FPGA 1106, with a top-level view of which shownin FIG. 11D, may be configured to interface with the RF module 1132. Forexample, the FPGA 1106 is configured to one or more of control thetransceiver module, control the RF discrete pins, control the ADCmodule, generate the IF signal for the RF module 1132, and acquire ADC(analog-digital conversion) output samples, synchronized with thegenerated IF signal. Further, in some embodiments, the FPGA 1106 isconfigured to process the ADC output samples to generate the basebanddata. In addition, in some embodiments, the FPGA 1106 may be configuredto interface with the microcontroller or microprocessor 1108. Forexample, the FPGA 1106 may start RF transmission (per frame) uponcommand from microprocessor 1108, save baseband data to local RAM, perframe, for microprocessor 1108 to read, allow microprocessor 1108read/write transactions towards configuration memory, provide a debuginterface for the microprocessor 1108, and/or allow microprocessor 1108to change configuration settings using a dedicated memory.

In some embodiments, the FPGA can support up to 128 frequencies,allowing for a different gain and dwell time per frequency. In someembodiments, power consumption can be minimized by using several clockfrequencies within the design and gating unused clock signals. In someembodiments, microprocessor data acquisition can be performed using aseparate clock, allowing the shut-down of the entire control &processing pipe while reading the data.

In some embodiments, the sensor disclosed herein may comprise anaccelerometer and the accelerometer may be used to determine one or moreof the physical activity, posture and respiration rate of a patientwearing the sensor. For example, a three-axis (3D) accelerometer 1122may be used to acquire data on patient movements and posture as well asthe respiration rate, and a processor (of the sensor or an externalserver, for example) receiving the acquired data may use the data (e.g.,in conjunction with data obtained by the sensor such as ECG data orRF-based measurements) to determined physiological parameters of thepatient, such as the lung fluid level of the patient. The 3Daccelerometer 1122 may be used to aid RF and/or ECG analysis bydetecting different types of motion segments in the recording so thatthe conditions of the measurements of the RF and/or the ECG may beinterpreted/analyzed accordingly. For example, in some embodiments, RFand/or ECG measurements may be performed while the patient wearing thesensor is active or at rest. The analysis of the RF and/or ECG data maythen depend on the state of the patient's physical activity (e.g., atrest, low intensity activity, high intensity activity, etc.). In suchembodiments, the accelerator may be used to identify the patient'sphysical state so as to properly analyze and interpret the RF and/or ECGmeasurements.

In some embodiments, the accelerometer 1122 may also contain an internaltap detector, which may be used for generating a patient triggered event(e.g., using “double tap” feature). The acceleration signal can be usedto calculate respiration rate. FIG. 11A shows an example embodiment of asensor comprising a 3D accelerometer 1122, RF antennas 1104 a, 1104 b,ECG processing circuitry coupled to ECG electrodes, a microcontroller1108 (which may be alternatively referred as microprocessor throughoutthis disclosure) and a telemetry (e.g., Bluetooth®) 1114. In suchembodiments, for example, the micro-controller 1108 may receive data onpatient respiration rate, movements, posture, ECG as well as RF-basedmeasurements of the patient and process, and/or transmit to an externalprocessor via the telemetry 1114 for further processing, to determine aphysiological parameter of the patient. As an example, themicro-controller 1108 of the sensor may cause the Bluetooth® telemetry1114 to transmit the noted data and measurements to an external serverwhich in turn analyzes the RF measurements, the ECG, posture, movement,and/or respiration rate data to determine the lung fluid level of thepatient. As an another example, the external server may analyze ECG datato determine patient health conditions related to one or more of a heartrate, atrial fibrillation, flutter, supraventricular tachycardia,ventricular tachycardia, pause, atrioventricular (AV) block, ventricularfibrillation, bigeminy, trigeminy, ventricular ectopic beats,supraventricular ectopic beats (SVEB), bradycardia, and tachycardia. Thedetermination of patient physiological health parameters (e.g., lungfluid level or the above-noted health conditions) may allow the serverto provide a notification on health-related events of the patientwearing the sensor for which the data came. For example, upondetermining an arrhythmia condition from data received from a sensor, anexternal server may provide a notification indicating a cardiac eventwith respect to the wearer of the sensor that transmitted the data.

In some embodiments, the sensor may also include a temperature sensor,conductance sensor, a pressure sensor, a respiration sensor, SPO2,and/or a light sensor. For example, a respiration sensor can include anaccelerometer configured to monitor the patient's chest movements, e.g.,during certain portions of the day and/or night or during an RFmeasurement. For instance, a 3D multi-axis, multi-channel accelerometercan be configured to, on a first channel, monitor for a patient movementand/or posture, and on a second, different channel, monitor the chestmovements of the patient to determine respiration rate and other relateddata. Alternatively, a respiration accelerometer can be provided in thedevice that is separate from a posture sensing accelerometer. In someexamples, the respiration rate measurement can be based on the operationof a tri-axis micro-electromechanical system (MEMS) accelerometer withinthe device mounted on the patient's torso. The accelerometer can measureprojections of the gravity vector on its intrinsic axes. From thesemeasurements, a respiration rate can be derived based on measuredquasi-periodic changes of the projections that occur due to respirationmovements of the patient's rib cage.

In other examples, the respiration rate and/or other respiration datacan be derived from the RF signals themselves. For example, dedicatedrespiration circuitry can be provided and/or the processor can beconfigured with instructions to cause the processor to monitor thereflected RF waves as described herein and determine respiration rateand related data therefrom. In some embodiments, respirationcharacteristics such as exhale vs. inhale times can also be measured viaan accelerometer and health conditions such as sleep apnea may bedetected from accelerometer measurements.

In some embodiments, RR, which denotes ventricular interbeat interval onECG, may be derived from ECG data and the RR accuracy can be improved byfusing the data from two or more of these RR measurement methods.

When using the disclosed sensor, in some embodiments, there arescenarios that involve the removal of the adhesive patch from the skinof a body, either by involving the transfer of sensors from old patientto new patient or when replacing faulty sensors. For example, when adevice is in a charger or on a patient in error, it can be disassociatedfrom the patient through a server action. Similarly, if the device isnewly assigned to a patient, the device can be associated with a newpatient through a server action. In some embodiments, certainoperational modes of the sensor may not include all aspects of thesensor's operational capability. For example, situations involvingautomatic built-in tests, regulation tests, debugging, handling when thesensor is faulty, etc., one or more features of the sensor may not beactivated or operational (or may operate differently than when thesensor is fully or normally operational) while the sensor itself isoperating. For example, when debugging a faulty system, in someembodiments, transmission may be conducted via a single specificfrequency by allowing configuring a specific frequency and triggeringstart/stop transmission.

Overall dimensions Smaller than about 55 mm × about 70 mm × about 17 mmMaximum weight Less than about 70 grams ECG attachment Embedded inadhesive patch Gel using hydrogel embedded in patch Device Ultrasonicsealing, tested according to IP67 liquid/dustproofing Package Contents:1 device, charging cradle, User manual and disposable patches; Patchesmust be packed appropriately to avoid glue dehydration. Labelling Deviceshould be labelled with serial number & FCC ID. Label must withstandenvironmental conditions according to IP67 Soft feel Rubber like feel,little or no sharp edges Push-Button Multipurpose; designed to be usedby technician; protected from accidental activation by the patient topreserve power; Used for reset, pairing and to initiate communicationLED Multipurpose; dual color; indicates battery status, pairing, errors,BT connection. Device-in-patch sensing electrical-connection BuzzerAudio notification, between about 1 and about 3 KHz and over about 60dBSPL intensity at a distance of 1 m. PCB placement Without screws andcase closure Drop protection Device is designed to comply with droptests according to standard IEC 60601-1 and 60601-1-11

FIG. 12 illustrates an example heart monitoring device, e.g., medicaldevice 1200 that is external, ambulatory, and wearable by a patient1202, and configured to implement one or more configurations describedherein. For example, the medical device 1200 can be a non-invasivemedical device configured to be located substantially external to thepatient. Such a medical device 1200 can be, for example, an ambulatorymedical device that is capable of and designed for moving with thepatient as the patient goes about his or her daily routine. For example,the medical device 1200 as described herein can be bodily-attached tothe patient such as the LifeVest® wearable cardioverter defibrillatoravailable from ZOLL® Medical Corporation. In one example scenario, suchwearable defibrillators can be worn nearly continuously or substantiallycontinuously for two to three months at a time. During the period oftime in which they are worn by the patient, the wearable defibrillatorcan be configured to continuously or substantially continuously monitorthe vital signs of the patient and, upon determination that treatment isrequired, can be configured to deliver one or more therapeuticelectrical pulses to the patient. For example, such therapeutic shockscan be pacing, defibrillation, or transcutaneous electrical nervestimulation (TENS) pulses.

The medical device 1200 can include one or more of the following: agarment 1210, one or more sensing electrodes 1212 (e.g., ECGelectrodes), one or more therapy electrodes 1214 a and 1214 b(collectively referred to herein as therapy electrodes 1214), a medicaldevice controller 1220, a connection pod 1230, a patient interface pod1240, a belt 1250, or any combination of these. In some examples, atleast some of the components of the medical device 1200 can beconfigured to be affixed to the garment 1210 (or in some examples,permanently integrated into the garment 1210), which can be worn aboutthe patient's torso.

The medical device controller 1220 can be operatively coupled to thesensing electrodes 1212, which can be affixed to the garment 1210, e.g.,assembled into the garment 1210 or removably attached to the garment,e.g., using hook and loop fasteners. In some implementations, thesensing electrodes 1212 can be permanently integrated into the garment1210. The medical device controller 1220 can be operatively coupled tothe therapy electrodes 1214. For example, the therapy electrodes 1214can also be assembled into the garment 1210, or, in someimplementations, the therapy electrodes 1214 can be permanentlyintegrated into the garment 1210.

Component configurations other than those shown in FIG. 12 are possible.For example, the sensing electrodes 1212 can be configured to beattached at various positions about the body of the patient 1202. Thesensing electrodes 1212 can be operatively coupled to the medical devicecontroller 1220 through the connection pod 1230. In someimplementations, the sensing electrodes 1212 can be adhesively attachedto the patient 1202. In some implementations, the sensing electrodes1212 and at least one of the therapy electrodes 1214 can be included ona single integrated patch and adhesively applied to the patient's body.

The sensing electrodes 1212 can be configured to detect one or morecardiac signals. Examples of such signals include ECG signals and/orother sensed cardiac physiological signals from the patient. In certainimplementations, the sensing electrodes 1212 can include additionalcomponents such as accelerometers, acoustic signal detecting devices,and other measuring devices for recording additional parameters. Forexample, the sensing electrodes 1212 can also be configured to detectother types of patient physiological parameters and acoustic signals,such as tissue fluid levels, heart vibrations, lung vibrations,respiration vibrations, patient movement, etc. Example sensingelectrodes 1212 include a metal electrode with an oxide coating such astantalum pentoxide electrodes, as described in, for example, U.S. Pat.No. 6,253,099 entitled “Cardiac Monitoring Electrode Apparatus andMethod,” the disclosure of which is incorporated by reference herein inits entirety.

In some examples, the therapy electrodes 1214 can also be configured toinclude sensors configured to detect ECG signals as well as otherphysiological signals of the patient. The connection pod 1230 can, insome examples, include a signal processor configured to amplify, filter,and digitize these cardiac signals prior to transmitting the cardiacsignals to the medical device controller 1220. One or more of thetherapy electrodes 1214 can be configured to deliver one or moretherapeutic defibrillating shocks to the body of the patient 1202 whenthe medical device 1200 determines that such treatment is warrantedbased on the signals detected by the sensing electrodes 1212 andprocessed by the medical device controller 1220. Example therapyelectrodes 1214 can include conductive metal electrodes such asstainless steel electrodes that include, in certain implementations, oneor more conductive gel deployment devices configured to deliverconductive gel to the metal electrode prior to delivery of a therapeuticshock.

In some implementations, medical devices as described herein can beconfigured to switch between a therapeutic medical device and amonitoring medical device that is configured to only monitor a patient(e.g., not provide or perform any therapeutic functions). For example,therapeutic components such as the therapy electrodes 1214 andassociated circuitry can be optionally decoupled from (or coupled to) orswitched out of (or switched in to) the medical device. For example, amedical device can have optional therapeutic elements (e.g.,defibrillation and/or pacing electrodes, components, and associatedcircuitry) that are configured to operate in a therapeutic mode. Theoptional therapeutic elements can be physically decoupled from themedical device as a means to convert the therapeutic medical device intoa monitoring medical device for a specific use (e.g., for operating in amonitoring-only mode) or a patient. Alternatively, the optionaltherapeutic elements can be deactivated (e.g., by means or a physical ora software switch), essentially rendering the therapeutic medical deviceas a monitoring medical device for a specific physiologic purpose or aparticular patient. As an example of a software switch, an authorizedperson can access a protected user interface of the medical device andselect a preconfigured option or perform some other user action via theuser interface to deactivate the therapeutic elements of the medicaldevice.

FIG. 13 illustrates a sample component-level view of the medical devicecontroller 1220. As shown in FIG. 13 , the medical device controller1220 can include a therapy delivery circuit 1302, a data storage 1304, anetwork interface 1306, a user interface 1308, at least one battery1310, a sensor interface 1312, an alarm manager 1314, and least oneprocessor 1318. A patient monitoring medical device can include amedical device controller 1220 that includes like components as thosedescribed above, but does not include the therapy delivery circuit 1302(shown in dotted lines).

The therapy delivery circuit 1302 can be coupled to one or moreelectrodes 1320 configured to provide therapy to the patient (e.g.,therapy electrodes 1214 as described above in connection with FIG. 12 ).For example, the therapy delivery circuit 1302 can include, or beoperably connected to, circuitry components that are configured togenerate and provide the therapeutic shock. The circuitry components caninclude, for example, resistors, capacitors, relays and/or switches,electrical bridges such as an h-bridge (e.g., including a plurality ofinsulated gate bipolar transistors or IGBTs), voltage and/or currentmeasuring components, and other similar circuitry components arrangedand connected such that the circuitry components work in concert withthe therapy delivery circuit and under control of one or more processors(e.g., processor 1318) to provide, for example, one or more pacing ordefibrillation therapeutic pulses.

Pacing pulses can be used to treat cardiac arrhythmias such asbradycardia (e.g., less than 30 beats per minute) and tachycardia (e.g.,more than 150 beats per minute) using, for example, fixed rate pacing,demand pacing, anti-tachycardia pacing, and the like. Defibrillationpulses can be used to treat ventricular tachycardia and/or ventricularfibrillation.

The capacitors can include a parallel-connected capacitor bankconsisting of a plurality of capacitors (e.g., two, three, four or morecapacitors). These capacitors can be switched into a series connectionduring discharge for a defibrillation pulse. For example, fourcapacitors of approximately 650 uF can be used. The capacitors can havebetween 350 to 500 volt surge rating and can be charged in approximately15 to 30 seconds from a battery pack.

For example, each defibrillation pulse can deliver between 60 to 180joules of energy. In some implementations, the defibrillating pulse canbe a biphasic truncated exponential waveform, whereby the signal canswitch between a positive and a negative portion (e.g., chargedirections). This type of waveform can be effective at defibrillatingpatients at lower energy levels when compared to other types ofdefibrillation pulses (e.g., such as monophasic pulses). For example, anamplitude and a width of the two phases of the energy waveform can beautomatically adjusted to deliver a precise energy amount (e.g., 150joules) regardless of the patient's body impedance. The therapy deliverycircuit 1302 can be configured to perform the switching and pulsedelivery operations, e.g., under control of the processor 1318. As theenergy is delivered to the patient, the amount of energy being deliveredcan be tracked. For example, the amount of energy can be kept to apredetermined constant value even as the pulse waveform is dynamicallycontrolled based on factors such as the patient's body impedance whichthe pulse is being delivered.

The data storage 1304 can include one or more of non-transitory computerreadable media, such as flash memory, solid state memory, magneticmemory, optical memory, cache memory, combinations thereof, and others.The data storage 1304 can be configured to store executable instructionsand data used for operation of the medical device controller 1220. Incertain implementations, the data storage can include executableinstructions that, when executed, are configured to cause the processor1318 to perform one or more functions.

In some examples, the network interface 1306 can facilitate thecommunication of information between the medical device controller 1220and one or more other devices or entities over a communications network.For example, where the medical device controller 1220 is included in anambulatory medical device (such as medical device 1200), the networkinterface 1306 can be configured to communicate with a remote computingdevice such as a remote server or other similar computing device. Thenetwork interface 1306 can include communications circuitry fortransmitting data in accordance with a Bluetooth® wireless standard forexchanging such data over short distances to an intermediary device(s)(e.g., a base station, a “hotspot” device, a smartphone, a tablet, aportable computing device, and/or other devices in proximity of thewearable medical device 100). The intermediary device(s) may in turncommunicate the data to a remote server over a broadband cellularnetwork communications link. The communications link may implementbroadband cellular technology (e.g., 2.5G, 2.75G, 3G, 4G, 5G cellularstandards) and/or Long-Term Evolution (LTE) technology or GSM/EDGE andUMTS/HSPA technologies for high-speed wireless communication. In someimplementations, the intermediary device(s) may communicate with aremote server over a Wi-Fi™ communications link based on the IEEE 802.11standard.

In certain implementations, the user interface 1308 can include one ormore physical interface devices such as input devices, output devices,and combination input/output devices and a software stack configured todrive operation of the devices. These user interface elements may rendervisual, audio, and/or tactile content. Thus the user interface 1308 mayreceive input or provide output, thereby enabling a user to interactwith the medical device controller 1220.

The medical device controller 1220 can also include at least one battery1310 configured to provide power to one or more components integrated inthe medical device controller 1220. The battery 1310 can include arechargeable multi-cell battery pack. In one example implementation, thebattery 1310 can include three or more 13200 mAh lithium ion cells thatprovide electrical power to the other device components within themedical device controller 1220. For example, the battery 1310 canprovide its power output in a range of between 20 mA to 1000 mA (e.g.,40 mA) output and can support 24 hours, 48 hours, 72 hours, or more, ofruntime between charges. In certain implementations, the batterycapacity, runtime, and type (e.g., lithium ion, nickel-cadmium, ornickel-metal hydride) can be changed to best fit the specificapplication of the medical device controller 1220.

The sensor interface 1312 can be coupled to one or more sensorsconfigured to monitor one or more physiological parameters of thepatient. As shown, the sensors may be coupled to the medical devicecontroller 1220 via a wired or wireless connection. The sensors caninclude one or more electrocardiogram (ECG) electrodes 1322 (e.g.,similar to sensing electrodes 1212 as described above in connection withFIG. 1 ), heart vibrations sensors 1324, and tissue fluid monitors 1326(e.g., based on ultra-wide band radiofrequency devices).

The ECG electrodes 1322 can monitor a patient's ECG information. Forexample, the ECG electrodes 1322 can be galvanic (e.g., conductive)and/or capacitive electrodes configured to measure changes in apatient's electrophysiology to measure the patient's ECG information.The ECG electrodes 1322 can transmit information descriptive of the ECGsignals to the sensor interface 1312 for subsequent analysis.

The heart vibrations sensors 1324 can detect a patient's heart vibrationinformation. For example, the heart vibrations sensors 1324 can beconfigured to detect heart vibration values including any one or all ofS1, S2, S3, and S4. From these heart vibration values, certain heartvibration metrics may be calculated, including any one or more ofelectromechanical activation time (EMAT), percentage of EMAT (% EMAT),systolic dysfunction index (SDI), and left ventricular systolic time(LVST). The heart vibrations sensors 1324 can include an acoustic sensorconfigured to detect vibrations from a subject's cardiac system andprovide an output signal responsive to the detected heart vibrations.The heart vibrations sensors 1324 can also include a multi-channelaccelerometer, for example, a three channel accelerometer configured tosense movement in each of three orthogonal axes such that patientmovement/body position can be detected and correlated to detected heartvibrations information. The heart vibrations sensors 1324 can transmitinformation descriptive of the heart vibrations information to thesensor interface 1312 for subsequent analysis.

The tissue fluid monitors 1326 can use radio frequency (RF) basedtechniques to assess fluid levels and accumulation in a patient's bodytissue. For example, the tissue fluid monitors 1326 can be configured tomeasure fluid content in the lungs, typically for diagnosis andfollow-up of pulmonary edema or lung congestion in heart failurepatients. The tissue fluid monitors 1326 can include one or moreantennas configured to direct RF waves through a patient's tissue andmeasure output RF signals in response to the waves that have passedthrough the tissue. In certain implementations, the output RF signalsinclude parameters indicative of a fluid level in the patient's tissue.The tissue fluid monitors 1326 can transmit information descriptive ofthe tissue fluid levels to the sensor interface 1312 for subsequentanalysis.

The sensor interface 1312 can be coupled to any one or combination ofsensing electrodes/other sensors to receive other patient dataindicative of patient parameters. Once data from the sensors has beenreceived by the sensor interface 1312, the data can be directed by theprocessor 1318 to an appropriate component within the medical devicecontroller 1220. For example, if heart data is collected by heartvibrations sensor 1324 and transmitted to the sensor interface 1312, thesensor interface 1312 can transmit the data to the processor 1318 which,in turn, relays the data to a cardiac event detector. The cardiac eventdata can also be stored on the data storage 1304.

In certain implementations, the alarm manager 1314 can be configured tomanage alarm profiles and notify one or more intended recipients ofevents specified within the alarm profiles as being of interest to theintended recipients. These intended recipients can include externalentities such as users (patients, physicians, and monitoring personnel)as well as computer systems (monitoring systems or emergency responsesystems). The alarm manager 1314 can be implemented using hardware or acombination of hardware and software. For instance, in some examples,the alarm manager 1314 can be implemented as a software component thatis stored within the data storage 1304 and executed by the processor1318. In this example, the instructions included in the alarm manager1314 can cause the processor 1318 to configure alarm profiles and notifyintended recipients using the alarm profiles. In other examples, alarmmanager 1314 can be an application-specific integrated circuit (ASIC)that is coupled to the processor 1318 and configured to manage alarmprofiles and notify intended recipients using alarms specified withinthe alarm profiles. Thus, examples of alarm manager 1314 are not limitedto a particular hardware or software implementation.

In some implementations, the processor 1318 includes one or moreprocessors (or one or more processor cores) that each are configured toperform a series of instructions that result in manipulated data and/orcontrol the operation of the other components of the medical devicecontroller 1220. In some implementations, when executing a specificprocess (e.g., cardiac monitoring), the processor 1318 can be configuredto make specific logic-based determinations based on input datareceived, and be further configured to provide one or more outputs thatcan be used to control or otherwise inform subsequent processing to becarried out by the processor 1318 and/or other processors or circuitrywith which processor 1318 is communicatively coupled. Thus, theprocessor 1318 reacts to specific input stimulus in a specific way andgenerates a corresponding output based on that input stimulus. In someexample cases, the processor 1318 can proceed through a sequence oflogical transitions in which various internal register states and/orother bit cell states internal or external to the processor 1318 may beset to logic high or logic low. As referred to herein, the processor1318 can be configured to execute a function where software is stored ina data store coupled to the processor 1318, the software beingconfigured to cause the processor 1318 to proceed through a sequence ofvarious logic decisions that result in the function being executed. Thevarious components that are described herein as being executable by theprocessor 1318 can be implemented in various forms of specializedhardware, software, or a combination thereof. For example, the processorcan be a digital signal processor (DSP) such as a 24-bit DSP processor.The processor can be a multi-core processor, e.g., having two or moreprocessing cores. The processor can be an Advanced RISC Machine (ARM)processor such as a 32-bit ARM processor. The processor can execute anembedded operating system, and include services provided by theoperating system that can be used for file system manipulation, display& audio generation, basic networking, firewalling, data encryption andcommunications.

In some embodiments, an ECG analysis system (e.g., arrhythmia monitoringsystem, heart failure status monitoring, and/or the like) may includeECG being acquired with a sensor on the body (e.g., external heartmonitoring device, which may include ECG electrodes and/or ECGprocessing circuitry, as described herein). Additionally oralternatively, a neural net may process the ECG signal(s) to identifyand flag reportable intervals (e.g., ECG signal portions associated withat least one predetermined rhythm change, as descried herein).

In some embodiments, a reportable event may include an ECG snippet(e.g., ECG signal portion), which may include a rhythm change from onerhythm type to another. Additionally or alternatively, the network(e.g., neural network) may identify the rhythm change without having toclassify the specific rhythm type. For example, the network (e.g.,neural network) may detect rhythm type change from normal sinus rhythm(NSR) to Atrial Fibrillation (AFIB), from AFIB to NSR, from AFIB toAtrial Flutter (AFL), etc. Additionally or alternatively, every time arhythm change is identified, the ECG strip (e.g., ECG signal portion)containing the change may be marked and/or sent to a server (e.g.,remote computer system, as described herein).

In some embodiments, a rhythm change can be of several different types,e.g., (1) an actual change from one clinical condition to another (NSRto AFib, AFib to ventricular tachycardia (VT), etc.), (2) a change inmorphology RR-interval statistics or similar change in signalcharacteristics, and/or the like.

In some embodiments, an input to the neural network may be a vector ofraw ECG samples taken during a time window. For example, the time windowmay be 15-120 seconds, 15-60 second, and/or the like. Additionally oralternatively, the output of the network may be the location of a rhythmchange in the ECG strip (e.g., ECG signal portion, vector of ECGsamples, and/or the like). In some embodiments, the neural network maybe a deep convolutional neural network, recurrent network, attentionnetwork, and/or the like.

For example, the neural network may include the following parameters:(i) 7-10 convolutional layers; (ii) an input layer receiving 15-60seconds of ECG sampled at 100-500 Hz, with overall 1500 to 30000 nodesat the input layer.

In some embodiments, the neural network may include multiple “Siamese”branches that handle multiple ECG channels (e.g., with shared ordifferent parameters), and/or may include with additional layers thatintegrate these channels.

In some embodiments, the neural network may also integrate (e.g., takeas input and/or the like) data from additional sensors, e.g.,accelerometers, heart sounds, and/or the like.

In some embodiments, the neural network may detect ECG strips that aredifferent in rhythm type from the patient's pre-acquired ECG baseline.For example, such a baseline may be pre-set to the network based on highaccuracy ECG measurements in the clinic, may be automatically detectedby the device (e.g., external heart monitoring device and/or the like)during known rest times (e.g. during night hours), and/or the like.

In some embodiments, the network (e.g., neural network) may provideconfidence levels to each classifications. Additionally oralternatively, any decision may depend on both the confidence level andthe classification (e.g., the device may decide to transmit events whichthe device is not able to classify with high enough confidence).

In some embodiments, the neural network may be implemented on one of thefollowing: (a) ECG sensor (e.g., external heart monitoring device): Theneural network may be implemented using a specialized, low-power neuralnetwork (NN) processing hardware). For example, a specialized NNprocessor may implement the neural network directly in silicon (e.g.,not using a general purpose Von-Neumann machine). This architecture mayallow for implementing the neural network with very low powerconsumption. As such, the neural network may be integrated into thepatient worn sensor, relying on a small battery. (b) Gateway: The systemmay include a gateway device (e.g., a smartphone, an Android device, atable, a laptop, a portable and/or handheld device, and/or the like)that receives the raw data from the sensor, e.g. using Bluetooth (BT)transmission, and then sends that data to a remote server. The neuralnetwork may be implemented on this gateway. (c) Server (e.g., remotecomputer system): A first layer of processing at the server-side can beused to reduce the amount of data to be processed in the next processinglayers.

In some embodiments, the network (e.g., neural network) logic may besplit across the ECG sensor (e.g., external heart monitoring device),the gateway, and/or the server (e.g., remote computer system) in such away as to optimize power consumption, transmission limitations, otherconstraints, and/or the like while retaining the desired accuracylevels.

In some embodiments, in addition to the (first) neural network, theserver may include a second neural network. For example, the secondneural network may be larger, more accurate, and/or the like compared tothe (first) neural network. Additionally or alternatively, the secondneural network may also be capable of classifying the specificarrhythmia type.

In some embodiments, data transmission and processing may besignificantly reduced by sending only the flagged intervals (e.g., ECGsignal portions associated with at least one predetermined rhythmchange) to the server analysis layers (e.g., remote computer system).Additionally or alternatively, only flagged intervals may be processedat the server for arrhythmia type classification and/or presented totechnician for annotation.

In some embodiments, data reduction processing may also “hunt” for rarearrhythmia using non-NN processing (e.g., any suitable signal processingtechnique (e.g., separate from or including the second neural network)).

In some embodiments, a data reduction layer may “trickle” random strips(e.g., second ECG portions) to provide new annotated data for futurealgorithm training (e.g., training the first neural network, the secondneural network, and/or the like). For example, trickling may be random,may be based on the network confidence, and/or may be based on otherpredefined rules. In some embodiments, such trickling may allow forenlarging the available annotated dataset that can be used for improvingthe network performance. Additionally or alternatively, such tricklingmay allow for continued testing of the network (e.g., neural network)performance. For example, changes in the input distribution may affectthe detection performance, and/or continued testing may be useful. Assuch, the system may include a trickling mechanism.

In some embodiments, the system may employ rhythm bucketing. Forexample, after strips (e.g., ECG signal portions) are flagged (e.g.,determined to be associated with at least one predetermined rhythmchange), such strips (e.g., ECG signal portions) may be bucketed (e.g.,classified, categorized, grouped, clustered, and/or the like) intogroups of similar strips, e.g., for batch review by a technician. Forexample, such bucketing may be accomplished by using a network (e.g.,neural network), by commonality/similarity of features (e.g., vectorsrepresentations), and/or the like. In some embodiments, bucketing may beshown to technicians (e.g., using a dedicated user interface, such as agraphical user interface (GUI) and/or the like) to aid rapid humanprocessing and interrogation (e.g., of the ECG signal portions in eachbucket).

In some embodiments, Independent Diagnostic Testing Facility (IDTF)technicians may currently review every reported arrhythmia, and theremay not be a great advantage to telling the technicians what thetechnicians are seeing (e.g., identifying a rhythm change, type ofarrhythmia, and/or the like). In some embodiments, the amount of datadisplayed to such technicians may be reduced, e.g., to justidentified/determined rhythm changes, am amount of data per patient maybecome more reasonable/manageable (e.g., an average of approximately 10minutes per day). Additionally or alternatively, bucketing the events(e.g., ECG signal portions associated with and/or identified ascontaining at least one rhythm change) into classes may enable thetechnicians to review (e.g., classify, annotate, and/or the like) suchevents en-batch.

In some embodiments, a system (e.g., arrhythmia monitoring system, heartfailure status monitoring system, and/or the like) may be part of an ECGambulatory monitoring service that employs a group of ECG techniciansfor reviewing and analyzing ECG strips (e.g., ECG signal portions).Additionally or alternatively, such technicians may have differentlevels of experience and expertise.

In some embodiments, the system (e.g., arrhythmia monitoring system,heart failure status monitoring system, and/or the like) may monitor theoutput of the technicians' work and/or identifies inconsistencies. Forexample, if an inconsistency/anomaly is detected by the system, therelevant strip (e.g., ECG signal portion) may be routed to a seniortechnician (e.g., supervisor and/or the like) for additional review.

In some embodiments, the system (e.g., arrhythmia monitoring system,heart failure status monitoring system, and/or the like) may includeand/or uses a deep learning network (e.g., neural network). In someembodiments, the network (e.g., neural network) may accepts thefollowing inputs: (a) The current ECG strip e.g., ECG signal portion)under review. (b) The result of the ECG technician analysis (e.g.,annotation data). The technician's ECG analysis (e.g., annotation data)may be in form of a text describing the rhythm (e.g., “normal sinusrhythm,” “atrial fibrillation onset,” etc.).

In some embodiments, the network output may include a numeric metricreflecting the plausibility of the decision, taking into account thebody of previous decisions which were used for in the training phase.

In some embodiments, the system (e.g., neural network of the arrhythmiamonitoring system, heart failure status monitoring system, and/or thelike) may have a training stage (e.g., when system (e.g., the neuralnetwork of thereof) learns based on a training dataset) and a productionstage (e.g., when the system (e.g., the neural network of thereof) isused to evaluate technicians' decisions online.

In some embodiments, for each technician, a different network (e.g.,neural network) may be trained based only on other technicians decisions(e.g., to remove “self-persuasion”).

In some embodiments, the system (e.g., arrhythmia monitoring system,heart failure status monitoring system, and/or the like) may be set toalert only when there is a deviation with high confidence (e.g., lowplausibility).

In some embodiments, over time, conditions affecting the ECG input maychange. For example, the characteristics of the populations of subjectsmay change, e.g., as new populations are indicated for ECG evaluation.Additionally or alternatively, hardware changes may occur over time. Insome embodiments, based on this change over time, the performance of theneural network might deteriorate (e.g., accuracy and/or sensitivity maydrop).

In some embodiments, the ECG technicians only review the ECG strips(e.g., ECG signal portions) that were detected by the network (e.g.,neural network), so such technician may have no indication of thisreduction in performance. In some embodiments, a trickling mechanism maybe useful in order to detect such performance deterioration, enableperformance improvement by generating a continual stream of annotatedsamples representing the change in conditions that may be used toimprove the network, and/or the like.

In some embodiments with such a trickling mechanism, the technicians maybe presented with some ECG strips (e.g., ECG signal portions) that wherenot flagged by the neural network for review (e.g., the “trickle”).

In some embodiments, techniques of sampling strips (e.g., ECG signalportions) for review (e.g., generating the “trickle”) may be (a) alow-percentage of the strips randomly sent for analysis, (b) a portionof those strips that are not noise/NSR or those that are different fromsome norm or those that are suspect of being rare arrhythmia events(blocks, etc.), (c) those strips which are flagged as low confidence bythe neural network (e.g., using a special network for confidenceprediction or other techniques such as clustering or nearest-neighbors).

In some embodiments, if the original performance of the neural networkis preserved, the technician review of the “trickle” may be expected toyield only a low proportion of clinical findings, and/or the like.Additionally or alternatively, a deterioration in performance of theneural network may result in an increase in the clinical yield of thetrickle. In some embodiments, this change may be monitored. For example,when such change is observed, a retraining may be initiated. In someembodiments, the retraining may utilize the new annotation done by thetechnicians on the “trickle” that has accumulated up to that point.

In some embodiments, the “trickle” may be used as input to a server-sidesupervising neural network, which may be tasked to detect a change inthe distribution of the inputs.

In some embodiments, two different sensors (e.g., a first set ofelectrodes (e.g., of a first sensor device) and a second set ofelectrodes (e.g., of a second sensor device) separate from the first setof electrodes) may carry information on the same phenomenon. In someembodiments, the first sensor (e.g., with the first set of electrodes)may be more suitable for human interpretation, contain more information,have better quality, and/or the like (e.g., compared to the secondsensor). Additionally or alternatively, the second sensor may allow(e.g., be suitable for) machine detection of the desired phenomenon atsuitable performance, but the output of the second sensor may beunsuitable for human interpretation.

In some embodiments, an annotated reference (e.g., annotationsassociated with a historical collection of ECG signal portions) may beused for supervised learning. In some embodiments, to obtain such anannotated reference, two sensors may be deployed simultaneously on thesubject (e.g., patient). For example, annotation data may be generatedbased on the first sensor (e.g., by human reviewers/technicians).Additionally or alternatively, the annotation data obtained may be usedto train the network (e.g., neural network) using sensor data (e.g., ECGsignals and/or samples thereof) from the second sensor. In someembodiments, e.g., if multiple leads or multiple sensors (e.g.,wrist-watch, wearable patch, etc.) are available, one lead (e.g., a morereliable, accurate, suitable for human interpretation, and/or the likelead) may be used to annotate the data for the other lead (e.g., lessreliable, accurate, suitable for human interpretation, and/or the likelead).

For example, a lead II ECG signal (e.g., the reference signal from afirst sensor/set of electrodes) may be suitable for human AFIB detectionand interpretation. Additionally or alternatively, a patch sensor may bepositioned on the left axillary area (e.g., the target sensor/set orelectrodes) may be less suitable for human AFIB detection, but may stillcarry relevant information that can be used by machine learningalgorithms (e.g., neural networks).

In some embodiments, an annotated reference database maybe generatedusing the lead II signal, and such database may be used to train thepatch sensor.

In some embodiments, there may be situations in which the amount of dataavailable for training the network may be small. For example, this maybe due to (1) lack of enough recorded data, but where the recorded datais annotated, e.g., when a new sensor version is being used or adeployment method has changed and not enough data has yet beencollected; (2) availability of enough recorded raw data, but lack ofmanual labels (e.g., due to time, budget, or expertise constraints)

In some embodiments, to address these problems, the following methodscan be applied (e.g., individually or in tandem): (1) The network (e.g.,neural network) may be trained for a different task for which enoughdata and annotation is available (e.g., to identity r-peaks or measureheart rate). The resulting network (e.g., neural network) may then beused for the target task and fine-tuned (e.g., using the limitedannotated dataset). (2) Additionally or alternatively, a network (e.g.,neural network) may be trained on data from a specific lead (e.g., forwhich a large amount of annotated data is available), and the trainedmodel (e.g., neural network) may be applied to a target lead (e.g., forwhich a limited amount of annotated data is available). The base model(e.g., neural network) may be than fined-tuned for the target lead. (3)Additionally or alternatively, when manually labeled data is notavailable or is available in small quantities, training may utilizesignal representations that are learned using a self-supervised proxytasks on large amounts of unannotated data. These tasks can be, forexample: (a) predicting the representation of the signal or the rawsignal at different times in the future relative to the current strip(e.g., ECG signal portion), (b) training based on measuring predictionerror or using contrastive loss that tries to differentiate betweenstrips (e.g., ECG signal portions) that are more similar than others(e.g. based on their time distance from the predicted strip, thembelonging to different patients, and/or the like), (c) predicting therelative time from which a short test strip (e.g., ECG signal portion)was taken relative to another strip, (d) predicting signal features suchas heartrate (HR) for which there are other known algorithms.

In some embodiments, augmentation may be used to artificially enrich theannotated dataset for training. Such a method may be based on modifying(e.g., augmenting) the input signal in such a way that its labels arenot modified or are modified in a known way.

For example, for ECG classification, the following augmentation methodsmay be used: (1) linear combinations of different leads (to simulatesmall differences in positioning of the leads, or extrapolate tounavailable leads); (2) time warping (e.g., signal dependent timedilations) to simulate slightly modified heart rates; (3) filtering(e.g. using short time Fourier transforms); (4) adding noise (e.g.,typical and/or expected noise); (5) inversion of the signal; (6)Augmentation with style transfer (e.g., using “style transfer” and othermethods to augment arrhythmia events (e.g., rare arrhythmia events)while retaining their arrhythmia and using them for training, which maybe useful for training deep learning (DL) nets (e.g., neural network) inorder to increase the effectiveness (e.g., amount) of data, which mayallow projecting beat morphology onto different rhythms).

In some embodiments, the network (e.g., neural network) may accept thefollowing inputs: (a) current ECG strip for detection/classificationand/or (b) ECG context information. For example, the context informationmay include the following: (a) A baseline ECG strip (e.g., ECG signalportion) of the same patient. For example, such a strip may be from atime known or estimated to be normal (e.g., at sleep time, rest time,and/or the like) or to have some know characteristics. Such context maybe the raw data (e.g., ECG signal portion) or some representation of it(e.g., a vector representation built (e.g., generated) by a neuralnetwork). (b) A representation of all or some of patient's measurementsin the past that are aggregated as a reference (e.g., reference vector)for the current tested strip (e.g., current ECG signal portion). (c)Calibration measurements using high precision ECG (e.g., a 12-lead ECGin the clinic).

In some embodiments, the context data may include multiple time scales.For example, the network (e.g., neural network) may inspect ECG dataover different time scales to determine a classification. Additionallyor alternatively, the network (e.g., neural network) may integrate dataover one or more time periods (e.g., a month, day, the current ECG strip(e.g., a 30-second ECG signal portion)) to take into account trends overtime and patient specific baseline.

While various inventive embodiments have been described and illustratedherein, those of ordinary skill in the art will readily envision avariety of other means and/or structures for performing the functionand/or obtaining the results and/or one or more of the advantagesdescribed herein, and each of such variations and/or modifications isdeemed to be within the scope of the inventive embodiments describedherein. More generally, those skilled in the art will readily appreciatethat all parameters, dimensions, materials, and configurations describedherein are meant to be an example and that the actual parameters,dimensions, materials, and/or configurations will depend upon thespecific application or applications for which the inventive teachingsis/are used. Those skilled in the art will recognize, or be able toascertain using no more than routine experimentation, many equivalentsto the specific inventive embodiments described herein. It is,therefore, to be understood that the foregoing embodiments are presentedby way of example only and that, within the scope of the appended claimsand equivalents thereto, inventive embodiments may be practicedotherwise than as specifically described and claimed. Inventiveembodiments of the present disclosure are directed to each individualfeature, system, article, material, kit, and/or method described herein.In addition, any combination of two or more such features, systems,articles, materials, kits, and/or methods, if such features, systems,articles, materials, kits, and/or methods are not mutually inconsistent,is included within the inventive scope of the present disclosure.Embodiments disclosed herein may also be combined with one or morefeatures, as well as complete systems, devices and/or methods, to yieldyet other embodiments and inventions. Moreover, some embodiments, may bedistinguishable from the prior art by specifically lacking one and/oranother feature disclosed in the particular prior art reference(s);i.e., claims to some embodiments may be distinguishable from the priorart by including one or more negative limitations.

Also, various inventive concepts may be embodied as one or more methods,of which an example has been provided. The acts performed as part of themethod may be ordered in any suitable way. Accordingly, embodiments maybe constructed in which acts are performed in an order different thanillustrated, which may include performing some acts simultaneously, eventhough shown as sequential acts in illustrative embodiments.

Any and all references to publications or other documents, including butnot limited to, patents, patent applications, articles, webpages, books,etc., presented anywhere in the present application, are hereinincorporated by reference in their entirety. Moreover, all definitions,as defined and used herein, should be understood to control overdictionary definitions, definitions in documents incorporated byreference, and/or ordinary meanings of the defined terms.

The indefinite articles “a” and “an,” as used herein in thespecification and in the claims, unless clearly indicated to thecontrary, should be understood to mean “at least one.”

The phrase “and/or,” as used herein in the specification and in theclaims, should be understood to mean “either or both” of the elements soconjoined, i.e., elements that are conjunctively present in some casesand disjunctively present in other cases. Multiple elements listed with“and/or” should be construed in the same fashion, i.e., “one or more” ofthe elements so conjoined. Other elements may optionally be presentother than the elements specifically identified by the “and/or” clause,whether related or unrelated to those elements specifically identified.Thus, as a non-limiting example, a reference to “A and/or B”, when usedin conjunction with open-ended language such as “comprising” can refer,in one embodiment, to A only (optionally including elements other thanB); in another embodiment, to B only (optionally including elementsother than A); in yet another embodiment, to both A and B (optionallyincluding other elements); etc.

As used herein in the specification and in the claims, “or” should beunderstood to have the same meaning as “and/or” as defined above. Forexample, when separating items in a list, “or” or “and/or” shall beinterpreted as being inclusive, i.e., the inclusion of at least one, butalso including more than one, of a number or list of elements, and,optionally, additional unlisted items. Only terms clearly indicated tothe contrary, such as “only one of” or “exactly one of,” or, when usedin the claims, “consisting of,” will refer to the inclusion of exactlyone element of a number or list of elements. In general, the term “or”as used herein shall only be interpreted as indicating exclusivealternatives (i.e. “one or the other but not both”) when preceded byterms of exclusivity, such as “either,” “one of,” “only one of,” or“exactly one of.” “Consisting essentially of,” when used in the claims,shall have its ordinary meaning as used in the field of patent law.

As used herein in the specification and in the claims, the phrase “atleast one,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified. Thus, as anon-limiting example, “at least one of A and B” (or, equivalently, “atleast one of A or B,” or, equivalently “at least one of A and/or B”) canrefer, in one embodiment, to at least one, optionally including morethan one, A, with no B present (and optionally including elements otherthan B); in another embodiment, to at least one, optionally includingmore than one, B, with no A present (and optionally including elementsother than A); in yet another embodiment, to at least one, optionallyincluding more than one, A, and at least one, optionally including morethan one, B (and optionally including other elements); etc.

In the claims, as well as in the specification above, all transitionalphrases such as “comprising,” “including,” “carrying,” “having,”“containing,” “involving,” “holding,” “composed of,” and the like are tobe understood to be open-ended, i.e., to mean including but not limitedto. Only the transitional phrases “consisting of” and “consistingessentially of” shall be closed or semi-closed transitional phrases,respectively.

As used herein, the terms “right”, “left”, “top”, and derivativesthereof shall relate to the invention as it is oriented in the drawingfigures. However, it is to be understood that the invention can assumevarious alternative orientations and, accordingly, such terms are not tobe considered as limiting. Also, it is to be understood that theinvention can assume various alternative variations and stage sequences,except where expressly specified to the contrary. It is also to beunderstood that the specific devices and processes illustrated in theattached drawings, and described in the following specification, areexamples. Hence, specific dimensions and other physical characteristicsrelated to the embodiments disclosed herein are not to be considered aslimiting.

As used herein, the terms “communication” and “communicate” refer to thereceipt or transfer of one or more signals, messages, commands, or othertype of data. For one unit or component to be in communication withanother unit or component means that the one unit or component is ableto directly or indirectly receive data from and/or transmit data to theother unit or component. This can refer to a direct or indirectconnection that can be wired and/or wireless in nature. Additionally,two units or components can be in communication with each other eventhough the data transmitted can be modified, processed, routed, and thelike, between the first and second unit or component. For example, afirst unit can be in communication with a second unit even though thefirst unit passively receives data, and does not actively transmit datato the second unit. As another example, a first unit can be incommunication with a second unit if an intermediary unit processes datafrom one unit and transmits processed data to the second unit. It willbe appreciated that numerous other arrangements are possible.

Although the subject matter contained herein has been described indetail for the purpose of illustration, it is to be understood that suchdetail is solely for that purpose and that the present disclosure is notlimited to the disclosed embodiments, but, on the contrary, is intendedto cover modifications and equivalent arrangements that are within thespirit and scope of the appended claims. For example, it is to beunderstood that the present disclosure contemplates that, to the extentpossible, one or more features of any embodiment can be combined withone or more features of any other embodiment.

Other examples are within the scope and spirit of the description andclaims. Additionally, certain functions described above can beimplemented using software, hardware, firmware, hardwiring, orcombinations of any of these. Features implementing functions can alsobe physically located at various positions, including being distributedsuch that portions of functions are implemented at different physicallocations.

What is currently claimed:
 1. An arrhythmia monitoring system,comprising: an external heart monitoring device for a patientcomprising: a plurality of electrocardiogram (ECG) electrodes configuredto sense surface ECG activity of the patient; ECG processing circuitryconfigured to process the surface ECG activity of the patient to provideat least one ECG signal for the patient on at least one ECG channel; andat least one first processor operatively connected to the at least oneECG channel, the at least one first processor configured to: receive theat least one ECG signal received via the at least one ECG channel, andtransmit the at least one ECG signal; a gateway device comprising: anon-transitory computer-readable medium comprising a rhythm changeclassifier, the rhythm change classifier comprising at least one neuralnetwork trained based on a historical collection of a plurality of ECGsignal portions with known rhythm change information; and at least onesecond processor operatively connected to the non-transitorycomputer-readable medium, the at least one second processor configuredto: receive the at least one ECG signal from the external heartmonitoring device, detect with the rhythm change classifier time datacorresponding to a predetermined rhythm change in the at least one ECGsignal, the time data comprising at least one of a start time, a timeinterval, or any combination thereof, determine based on the detectedtime data at least one ECG signal portion associated with the detectedtime data corresponding to the predetermined rhythm change in the atleast one ECG signal, and transmit the at least one determined ECGsignal portion to a remote computer system.
 2. The arrhythmia monitoringsystem of claim 1, wherein the at least one determined ECG signalportion comprises a plurality of determined ECG signal portions, whereinthe remote computer system is in communication with the gateway device,the remote computer system configured to: receive the plurality ofdetermined ECG signal portions from the external heart monitoringdevice, and analyze each respective determined ECG signal portion of theplurality of determined ECG signal portions to classify a respectiveclass for each respective determined ECG signal portion, wherein theclass for at least two respective determined ECG signal portionscomprises a first class.
 3. The arrhythmia monitoring system of claim 2,wherein the remote computer system is further configured to transmit atleast one message associated with the at least two respective determinedECG signal portions to a computing device associated with a technician.4. The arrhythmia monitoring system of claim 3, wherein the computingdevice associated with the technician is configured to display agraphical user interface for batch review of the at least two respectivedetermined ECG signal portions of the first class.
 5. The arrhythmiamonitoring system of claim 2, wherein analyzing each respectivedetermined ECG signal portion of the plurality of determined ECG signalportions to classify the respective class for each respective determinedECG signal portion comprises bucketing the plurality of determined ECGsignal portions into a plurality of buckets, wherein the first classcomprises a first bucket of the plurality of buckets.
 6. The arrhythmiamonitoring system of claim 5, wherein bucketing the plurality ofdetermined ECG signal portions into the plurality of buckets comprisesgrouping the plurality of determined ECG signal portions based on atleast one of an output of a neural network, a similarity of features ofthe plurality of determined ECG signal portions, a similarity of vectorrepresentations of the plurality of determined ECG signal portions, orany combination thereof.
 7. The arrhythmia monitoring system of claim 1,wherein the external heart monitoring device comprises a wearable patch.8. The arrhythmia monitoring system of claim 1, wherein the externalheart monitoring device comprises a wearable defibrillator.
 9. Thearrhythmia monitoring system of claim 1, wherein the remote computersystem is in communication with the gateway device, the remote computersystem configured to: receive the at least one determined ECG signalportion from the external heart monitoring device, and analyze the atleast one determined ECG signal portion to classify a type of arrhythmiafor the rhythm change in the at least one ECG signal.
 10. The arrhythmiamonitoring system of claim 1, wherein the at least one ECG channelcomprises at least a first ECG channel and a second ECG channel, whereinthe at least one ECG signal comprises at least a first ECG signalassociated with the first ECG channel and a second ECG signal associatedwith the second ECG channel, and wherein the first respective ECG signalis orthogonal to the second respective ECG signal.
 11. The arrhythmiamonitoring system of claim 1, further comprising: at least one sensorand associated sensor circuitry configured to sense non-ECG biometricdata of the patient, wherein the at least one second processor isfurther configured to detect with the rhythm change classifier thepredetermined rhythm change based on the at least one ECG signal and thenon-ECG biometric data of the patient.
 12. The arrhythmia monitoringsystem of claim 11, wherein the at least one sensor comprises at leastone of an accelerometer, a heart sound detector, or a combinationthereof, and wherein the non-ECG biometric data comprises at least oneof acceleration data, heart sound data, or any combination thereof. 13.The arrhythmia monitoring system of claim 11, wherein detecting thepredetermined rhythm change is further based on at least one of: atleast one baseline ECG signal portion of the patient; at least onereference vector of the patient; at least one calibration measurement ofthe patient, the at least one calibration measurement based on at leastone second ECG signal from second surface ECG activity sensed by asecond plurality of ECG electrodes, the second plurality of ECGelectrodes independent of the plurality of ECG electrodes of theexternal heart monitoring device; or at least one previous ECG signalportion.
 14. The arrhythmia monitoring system of claim 1, wherein the atleast one ECG channel comprises a plurality of ECG channels, wherein theat least one ECG signal comprises at least one respective ECG signalassociated with each respective ECG channel of the plurality of ECGchannels, wherein the at least one neural network comprises a pluralityof Siamese branches, each respective Siamese branch of the plurality ofSiamese branches associated with a respective ECG channel of theplurality of ECG channels, and wherein the at least one neural networkfurther comprises at least one further layer connected to the pluralityof Siamese branches.
 15. The arrhythmia monitoring system of claim 14,wherein each Siamese branch of the plurality of Siamese branchescomprises a plurality of convolutional layers, wherein dimensions ofeach of the plurality of convolutional layers of each respective Siamesebranch are the same as the dimensions of each of the plurality ofconvolutional layers of each other Siamese branch.
 16. The arrhythmiamonitoring system of claim 14, wherein the plurality of ECG channelscomprises a first ECG channel and a second ECG channel, wherein the atleast one ECG signal comprises a first respective ECG signal associatedwith the first ECG channel and a second respective ECG signal associatedwith the second ECG channel, and wherein the first respective ECG signalis orthogonal to the second respective ECG signal.
 17. An arrhythmiamonitoring system, comprising: an external heart monitoring device for apatient comprising: a plurality of electrocardiogram (ECG) electrodesconfigured to sense surface ECG activity of the patient; ECG processingcircuitry configured to process the surface ECG activity of the patientto provide at least one ECG signal for the patient on at least one ECGchannel; a non-transitory computer-readable medium comprising a rhythmchange classifier, the rhythm change classifier comprising at least oneneural network trained based on a historical collection of a pluralityof ECG signal portions with known rhythm change information; and atleast one processor operatively connected to the at least one ECGchannel and the non-transitory computer-readable medium, the at leastone processor configured to: receive the at least one ECG signalreceived via the at least one ECG channel, detect, with the rhythmchange classifier, time data corresponding to a predetermined rhythmchange in the at least one ECG signal, the time data comprising at leastone of a start time, a time interval, or any combination thereof,determine, based on the detected time data, at least one ECG signalportion associated with the detected time data corresponding to thepredetermined rhythm change in the at least one ECG signal, and transmitthe at least one determined ECG signal portion; and a remote computersystem in communication with the external heart monitoring device, theremote computer system configured to: receive the at least onedetermined ECG signal portion from the external heart monitoring device,and analyze each respective determined ECG signal portion of the atleast one determined ECG signal portion to classify a respective classfor each respective determined ECG signal portion.
 18. The arrhythmiamonitoring system of claim 17, wherein the at least one determined ECGsignal portion comprises a plurality of determined ECG signal portions,and wherein the class for at least two respective determined ECG signalportions comprises a first class.
 19. The arrhythmia monitoring systemof claim 18, wherein the remote computer system is further configured totransmit at least one message associated with the at least tworespective determined ECG signal portions to a computing deviceassociated with a technician, and wherein the computing deviceassociated with the technician is configured to display a graphical userinterface for batch review of the at least two respective determined ECGsignal portions of the first class.
 20. The arrhythmia monitoring systemof claim 18, wherein analyzing each respective determined ECG signalportion of the plurality of determined ECG signal portions to classifythe respective class for each respective determined ECG signal portioncomprises bucketing the plurality of determined ECG signal portions intoa plurality of buckets, wherein the first class comprises a first bucketof the plurality of buckets, and wherein bucketing the plurality ofdetermined ECG signal portions into the plurality of buckets comprisesgrouping the plurality of determined ECG signal portions based on atleast one of an output of a neural network, a similarity of features ofthe plurality of determined ECG signal portions, a similarity of vectorrepresentations of the plurality of determined ECG signal portions, orany combination thereof.